From Privacy Payment to Privacy Collaboration: How Cryptography Is Reshaping Trust and Freedom for Individuals, Businesses, and Society

This article is co-authored by BenFen and TX-SHIELD.

Blockchain transparency is the cornerstone of its trust model. Yet today, this very feature of radical openness has become one of the greatest obstacles to large-scale adoption. For the Web3 vision, transparency is a “double-edged sword”: it delivers verifiable trust, but at the cost of commercial confidentiality, personal financial privacy, and regulatory flexibility.

For enterprises, every on-chain settlement risks exposing critical supplier relationships, procurement costs, and compensation structures to competitors. For individual users, each on-chain payment permanently records and publicly reveals their spending behavior, asset holdings, and social relationships. For regulators, the challenge lies in finding a new equilibrium between “protecting public privacy” and “fulfilling financial compliance responsibilities”.

Transparency should not come at the expense of fundamental privacy. BenFen Public Chain (powered by TX-SHIELD) is officially launching its privacy payment functionality. This marks a paradigm shift—from an era of “trust through disclosure” to a new model of “trust through privacy.” This article will be presented in two parts. The first part explores how privacy payments address the pressing challenges faced today by businesses, individuals, and regulators. The second part looks ahead, unfolding a broader vision of the future: how, with privacy as a foundational layer, we can collectively build a comprehensive on-chain ecosystem—ranging from decentralized dark pools and confidential voting to sealed-bid auctions, and ultimately, entirely new forms of social collaboration.

TX-SHIELD is a technology company specializing in privacy payment algorithms. It provides one-stop, regulation-friendly and privacy-preserving solutions for public blockchains, stablecoin issuers, and decentralized exchanges (DEXs). www.tx-shield.com

Addressing Today’s Pain Points: Urgent Applications and Deep Dive into Privacy Payments

For B2B Enterprises: Privacy Payment as a Strategic Tool for Competitive Advantage and Compliance

1. Protecting Payroll Privacy: Using Deel as an Example of a “Strategic HR Tool”

In modern enterprises, particularly in the context of global talent competition among multinational companies, payroll systems are a core strategic component. However, when companies attempt to leverage transparent blockchain technology for payroll disbursement, they face a critical challenge.

Take Deel, the global payroll management platform and one of the fastest-growing SaaS companies in history, as an example. Deel’s core business lies in processing employee payments across the globe. The company has actively embraced blockchain technology: through its “Deel Crypto” service, it allows global employees and contractors to instantly withdraw payments in cryptocurrencies such as Bitcoin and USDC, effectively addressing the slow speed and high costs associated with traditional cross-border wire transfers. Yet, the very adoption of blockchain for payment efficiency exposes Deel to a severe transparency challenge.

For Deel and its tens of thousands of client companies, using a fully transparent blockchain for payroll would mean that every on-chain payment permanently reveals clients’ salary structures, compensation levels across countries and roles, and even individual employee incomes to the public. This exposure could trigger internal payroll conflicts, facilitate competitive poaching, and, most critically, leak Deel’s core commercial asset—its global payroll database—threatening the very foundation of its multi-billion-dollar valuation.

This real-world scenario highlights a clear and urgent demand on the enterprise side: global companies like Deel, which are early adopters of blockchain for efficient payroll, as well as numerous organizations seeking high-efficiency, transparent payroll management, require a payment solution that simultaneously: It must be capable of handling global salary payments efficiently, accurately and immutably by leveraging blockchain technology, and at the same time, like traditional financial systems, absolutely guarantee the commercial confidentiality of salary data to prevent catastrophic data breaches caused by the transparency of payment tools.

Already, pioneering Web3 companies and DAOs have successfully implemented payroll using privacy-focused technologies such as Zcash or the Aztec Network, demonstrating both the feasibility and necessity of privacy payments in payroll management. These practices suggest a paradigm shift: payroll confidentiality is evolving from a “contractual commitment” relying on systems and trust to a “cryptography-based technical guarantee”, marking a natural progression in modern enterprise management—particularly for globalized companies.

BenFen Chain (www.benfen.org) has launched its privacy payment functionality, powered by TX-SHIELD’s MPC (Multi-Party Computation) solution—the core engine specifically designed to meet the needs of enterprises like those described above. We have further proposed the construction of a comprehensive “enterprise-grade privacy payroll system.” Using BenFen Chain, companies can distribute salaries to employees around the world entirely on-chain. The system ensures that while all transactions are accurate and auditable, critical information, such as payment amounts, sender identities, and recipient identities, is fully concealed. For enterprises, this system acts as a “stealth strategic asset”: it supports efficient, transparent global payroll management while safeguarding the company’s most sensitive compensation data and business strategies. For the blockchain ecosystem, this approach represents a crucial step toward large-scale enterprise adoption. By directly addressing the core concerns of companies like Deel in managing highly sensitive payroll operations, it removes one of the most significant barriers to integrating blockchain technology into mainstream business applications.

2. Supply Chain Finance and Settlements: Using Apple and Foxconn as an Example to Solve the Dual Challenge of “Data Silos” and “Excessive Transparency”

The current challenges in supply chain finance fundamentally stem from the inability to efficiently transmit trust among participants. McKinsey’s Global Payments Report highlights that global supply chain finance faces an annual financing gap of trillions of dollars. At its core, this gap reflects a classic trust problem. Take the collaboration between Apple and its key supplier Foxconn as an example. The business data between the two form typical “data silos.” As a result, Foxconn’s accounts receivable—which reflect the real transactional context—cannot easily be validated to financial institutions for low-cost financing, leading to persistent challenges of costly and difficult funding across the supply chain. However, attempting to address this problem simply by adopting blockchain for the sake of data sharing and transparency introduces an even more critical issue: sensitive commercial information—such as Apple’s precise purchase prices, settlement terms, or production volumes of new products—would be fully exposed to all on-chain participants, including competitors. Competitors could then reverse-engineer Apple’s cost structure and product strategies. This new risk, born from attempting to solve an old problem, significantly hinders blockchain adoption in core enterprise business scenarios. Complex industrial supply chains, such as those in the automotive and consumer electronics sectors with vast supplier networks, are seeking innovative solutions that can simultaneously satisfy two seemingly contradictory goals: 1. Automate business processes and ensure verifiable data to meet trust requirements and unlock financing bottlenecks. 2. Fully protect all sensitive commercial details during collaboration, preventing the disclosure of core competitive advantages.

Industry exploration has validated the feasibility of this approach. The Baseline Protocol—a pioneering initiative driven by industry leaders such as EY, Microsoft, and AMD—demonstrates the potential. Its core innovation lies in leveraging advanced cryptographic techniques to synchronize business process states on a public blockchain, while keeping all sensitive commercial data confidential. This practice provides strong evidence that “competitive collaboration” through technology is possible: supply chain efficiency and trust can be improved collectively, while each participant maintains a robust data moat around their sensitive information.

BenFen Chain’s privacy payment feature is designed to tackle this complex challenge. Powered by TX-SHIELD’s verifiable privacy technology, we can build a trusted settlement layer on-chain. At this layer, suppliers can prove the validity and compliance of their accounts receivable to financial institutions without revealing sensitive information such as specific amounts, counterparty identities, or contract details. This provides financial institutions with the critical elements of trust needed for decision-making while keeping all commercial secrets fully protected. According to McKinsey’s predictions on digitalized supply chains, such solutions have the potential to significantly optimize cash flow efficiency, reducing settlement cycles from months to days. They establish next-generation infrastructure capable of revitalizing industrial chains and lowering overall financing costs.

3. Cross-Border B2B Payments and Settlements: Using SHEIN’s Global Supply Chain as an Example to Balance Efficiency, Cost, and Commercial Confidentiality

The traditional cross-border payment system has been constrained by speed and cost issues for decades. Payments processed through conventional correspondent banking channels such as SWIFT typically take 2 to 5 days to complete and involve high fees along with opaque intermediaries. Take the global fast-fashion giant SHEIN as an example. Its business model heavily relies on a fast-response global supply chain network composed of thousands of suppliers. As such, it is highly sensitive to both the efficiency and cost of cross-border payments. To address the shortcomings of traditional SWIFT-based systems, the industry naturally explores more efficient blockchain-based solutions such as stablecoins. However, even with the introduction of new tools aimed at improving efficiency, companies still face additional challenges posed by data privacy regulations across different jurisdictions (e.g., the EU’s GDPR) and complex compliance requirements. Moreover, for SHEIN, every payment recorded on a transparent blockchain could inadvertently reveal to competitors such as Temu its exact procurement prices for different suppliers, order allocation strategies, and even global cash flow paths—directly threatening its core competitive advantages. Companies urgently need a payment solution that combines the privacy protection of traditional banking systems, the settlement speed of stablecoins, and the compliance flexibility to navigate complex global regulatory frameworks.

BenFen Chain’s privacy payment functionality offers a new solution: a “B2B Cross-Border Privacy Settlement Layer.” This solution leverages TX-SHIELD’s privacy-preserving stablecoin technology, enabling near-instant on-chain cross-border transactions between enterprises, while concealing key information such as transaction amounts and counterparty identities. Only the transacting parties and authorized regulatory institutions can view this data. The expected outcomes of this solution are second-level settlements, over 50% reduction in average transaction fees, and zero leakage of core commercial information such as procurement strategies and sales channels—providing companies with a critical information advantage in the fiercely competitive global trade environment.

4. Privacy-Enhanced Treasury Management: Using MicroStrategy as an Example to Reshape Enterprise-Grade Blockchain Finance

As global enterprises accelerate digital transformation, treasury management has evolved from a back-office function into a core component of corporate strategy. It not only determines the security and liquidity of corporate funds but also directly affects capital structure, market signaling, and strategic decision-making. However, as more companies attempt to leverage blockchain and crypto assets to optimize their treasury structures, a new challenge emerges: while efficiency is improved, increased transparency introduces strategic exposure risks. Take the U.S.-listed company MicroStrategy as an example. As one of the world’s most prominent “blockchain treasury pioneers,” MicroStrategy has, since 2020, continuously acquired Bitcoin through bond financing and its own funds, incorporating it into the corporate balance sheet to hedge against inflation and optimize long-term value reserves. While this initiative represents a milestone in corporate asset allocation innovation, it also exposes the privacy challenges of public blockchain ledgers. Every fund transfer, asset rebalancing, or new purchase is recorded and analyzable on-chain in real time. Market observers can infer positions, cost ranges, and even potential future actions from transaction paths. This means that, alongside transparency and trust, blockchain also exposes corporate fund allocations, investment rhythms, and internal financial structures to global analysts. For a publicly listed company, this can trigger market volatility, speculative activity, and affect market value management, bond ratings, and precise control over capital market signals.

An increasing number of enterprises are no longer satisfied with the closed and slow nature of traditional treasury systems and are exploring blockchain-based real-time financial management. At the same time, they are concerned: if a public ledger is used, does this mean all fund flows are exposed to the public? Enterprises urgently need a treasury solution that combines the real-time settlement and automated scheduling advantages of blockchain with the privacy protection and audit compliance capabilities of traditional banking systems.

This is where BenFen Chain’s privacy payment system delivers unique value. Leveraging TX-SHIELD’s advanced MPC technology, BenFen Chain allows enterprises to perform on-chain operations such as asset transfers, reinvestments, and stablecoin liquidity allocation, while encrypting critical financial information—including transaction amounts, flows, and asset structures. The system automatically generates verifiable cryptographic proofs, with access restricted to authorized auditors or regulatory nodes. In this way, enterprises can execute treasury operations with blockchain-level efficiency while ensuring that sensitive information remains private “off-chain” but verifiable “on-chain.”

Under this architecture, if MicroStrategy were to manage its treasury on BenFen Chain, fund allocation, currency distribution, and asset rebalancing would no longer be exposed to the public market, while all on-chain operations remain compliant, auditable, and consistent with financial reporting. In other words, BenFen Chain enables enterprises to achieve a truly “cryptographically verifiable treasury”: transparent to regulators, private in execution.

5. DAO Treasury Management and Anonymous Funding: Using Uniswap DAO as an Example to Establish a “Strategic Shield” for Decentralized Organizations

Large DAOs, such as Uniswap DAO, often manage assets worth hundreds of millions or even billions of dollars. While complete transparency of the treasury is the cornerstone of community governance, it also brings practical challenges and pain points. When Uniswap DAO considers investing in an early-stage DeFi project, fully public negotiation and transfer details allow other whales or competitors to easily capture this information, causing the DAO’s entry cost to spike and rendering its investment strategy ineffective. A strong practical need thus arises: to provide the team or specific committees a certain degree of operational privacy when executing investments, grants, or contributor rewards, while maintaining effective community oversight of the overall health and fund usage of the treasury.

We envision providing DAOs with a dedicated “Treasury Privacy Vault” module on BenFen Chain. Through BenFen Chain’s privacy payment functionality, DAOs can make confidential investments, provide anonymous funding to projects, and issue private rewards to contributors. Subsequently, DAOs can leverage selective disclosure mechanisms to demonstrate to the community the overall reasonableness and compliance of fund usage over a given period, without revealing the details of each sensitive transaction. The technical feasibility of this solution has been validated by privacy-focused blockchain projects such as Aztec Network in their official use cases. Furthermore, the Messari report Understanding Decentralized Confidential Computing (DeCC) provides a theoretical framework from an industry perspective for such explorations, which aim to introduce data confidentiality while preserving decentralization. This solution empowers DAOs with a level of “commercial secrecy” protection akin to traditional companies, attracting traditional capital and institutional participants seeking strategic privacy into the Web3 ecosystem, thereby promoting further growth, maturity, and resilience of decentralized communities.

For Individual Users: Privacy Payments as the Technological Foundation of Financial Dignity and Personal Freedom

1. Protecting Daily Spending and Digital Life: Using Cryptocurrency Traders as an Example to Safeguard the “Digital Persona”

As global cryptocurrency payment infrastructure matures—from crypto cards launched by Visa and Mastercard to USDC integration by mainstream fintech companies such as PayPal and Revolut—more users are beginning to use cryptocurrencies for everyday transactions in the real world. Blockchain payments are evolving from a “niche activity for investors” into a “lifestyle for the general public.” Yet, one overlooked fact is that the full transparency of public chains is turning digital life into a glasshouse, easily observed by others.

Imagine a prominent cryptocurrency trader or Web3 entrepreneur whose everyday spending—from buying Starbucks coffee, paying for Netflix subscriptions, to purchasing holiday gifts for family—is all conducted through the same public wallet. This information can be easily tracked, aggregated, and cross-analyzed. Blockchain analytics firms, advertisers, and other entities could reconstruct the individual’s life trajectory, wealth distribution, interests, and even health status and family relationships. Such pervasive transparency threatens personal privacy, security, and even individual freedom. This is not an isolated case. The 2024 annual reports by Chainalysis and CipherTrace indicate that over 70% of on-chain identity profiles are built from users’ everyday transactions rather than large investment activities. CoinDesk and The Block have commented bluntly: “Without privacy, cryptocurrency payments will forever remain experimental.”

For Web3 payments to truly reach mainstream adoption, it is not enough to solve “efficiency and cost” problems; users must regain the privacy boundaries of their digital persona. In other words, privacy is a prerequisite for mass adoption, not an optional enhancement.

BenFen Chain’s privacy payment system is specifically designed to address this core pain point. Built on the BenFen Chain ecosystem, BenPay (www.benpay.com) enables users to make everyday micro-payments—such as dining, subscriptions, commuting, and online purchases—using applications within the ecosystem, including the BenPay Card. This allows stablecoin payments to integrate into daily life at low friction and high frequency. The system leverages advanced MPC technology to automatically conceal transaction amounts, timestamps, recipient information, and address correlations across different transactions. Additionally, BenFen Chain implements a selective disclosure mechanism, enabling users to grant restricted transaction visibility to merchants or regulators when needed, achieving a “verifiable but non-traceable” payment experience.

The anticipated impact of this solution goes beyond protecting individual privacy; it also drives the mainstream adoption of blockchain payments:

• For ordinary consumers, it restores cash-like freedom in the digital world—spending no longer serves as an entry point for data mining.
• For merchants, it enhances user trust and encourages more frequent Web3-native payment usage.
• For regulators, privacy is no longer synonymous with a “black box” but represents “bounded transparency.”

In the long term, privacy payments will become a foundational public infrastructure for digital life. They are not only a technological manifestation of personal data sovereignty but also a prerequisite for the Web3 economy to fully integrate into everyday society.

2. Protecting Purchases of Sensitive Goods and Services: Using Prescription Medication as an Example to Safeguard Personal Consumption Privacy

Amid increasing global regulatory pressures and the growing centralization of payment platforms, consumers are gradually losing the last line of privacy protection when purchasing legal but sensitive goods or services. Traditional payment systems, with centralized accounts and identity verification mechanisms, make every transaction traceable and analyzable. For individuals who need to regularly purchase prescription medications or mental health services, this is tantamount to having their personal lives exposed under a spotlight.

Blockchain payments, particularly stablecoin-based payments, offer a new possibility for these scenarios. They provide advantages such as instant settlement, cross-border accessibility, and immunity to intermediary freezes, making them especially suitable for sensitive consumption in a globalized digital lifestyle. Yet, the transparency of public ledgers introduces a critical challenge: when a purchase of medication or a mental health consultation is recorded on-chain, anyone can trace the transaction and infer personal health conditions, lifestyle, and economic status. This “transparency backfire” renders blockchain payments effectively unusable in precisely the scenarios where privacy is most needed.

Mainstream media outlets, including CoinDesk, have repeatedly noted: “Without privacy, cryptocurrency payments will struggle to gain adoption in mainstream consumer use cases.” This observation is corroborated by market behavior—privacy coins such as Monero are actively used in certain e-commerce and sensitive service sectors, demonstrating users’ strong demand for privacy protection. However, these solutions often conflict with regulatory requirements and struggle to enter mainstream payment systems.

BenFen Chain‘s privacy payment functionality addresses this critical need. Users can pay merchants through BenPay ecosystem applications such as BenPay Merchant, completing transactions on-chain while concealing key details such as transaction amounts, wallet addresses, and any information that could reveal specific consumption behavior. This allows users to safely access the services they need without worrying that the core details of their private lives will be permanently recorded on a public ledger, potentially creating future risks. We aim to further promote privacy payments as the default payment option for sensitive consumption scenarios. For users, it acts as a “consumption freedom talisman,” safeguarding the basic right to spend according to personal preference without harming others, and protecting personal dignity. For the blockchain payment ecosystem, it represents a critical step toward mainstream consumer adoption and meeting deep user needs, as it resolves a real-world pain point that exists even in traditional electronic payments, but is greatly amplified on blockchain.

3. Protecting Freelancers and Small Businesses: Using Upwork Designers as an Example to Provide Individuals with “Commercial Secrecy Protection”

In today’s rapidly growing digital economy and era of remote collaboration, freelancers and small businesses have an increasing demand for efficient and flexible cross-border settlements. More and more individuals are adopting blockchain and stablecoin payments as a new option for international invoicing and settlement. Whether they are creative workers on traditional platforms such as Upwork or Fiverr, or Web3 developers providing services for DAOs and NFT projects, they are increasingly accepting stablecoins like USDT and USDC. The reasons are practical: blockchain payments are borderless, settlements are instantaneous, fees are lower, and they bypass the complex processes and geographic restrictions of traditional banking. This has made stablecoins a new universal currency in the global freelance market.

However, as more individual economic activities are recorded on-chain, freelancers and small businesses are passively exposed on a fully transparent ledger. A top UI designer on Upwork, simultaneously serving cash-strapped startups and Fortune 500 clients, could have her pricing strategy, income fluctuations, and key client sources fully visible on-chain. This transparency allows competitors, clients, or third-party data analytics firms to easily glean critical business information. Such a state of “bare exposure on-chain” puts her at a disadvantage in price negotiations, potentially leads to trust and dispute issues, and directly undermines her commercial pricing power and market competitiveness. Individual operators similarly need to protect their commercial secrets—particularly pricing strategies and client relationships. In the traditional economy, this information is naturally protected by bank account privacy and commercial confidentiality systems. In the on-chain economy, however, they face almost no barriers.

This “transparency backfire” phenomenon is emerging as a key concern for the next generation of the individual economy. While centralized payment platforms such as Stripe and Payoneer offer some degree of privacy protection, users must fully entrust their data to the platform and cannot autonomously control their commercial information. The 2024 CoinDesk report also noted, “In the Web3 economy, privacy is no longer just a personal matter; it is a component of business competitiveness.” BenFen Chain’s privacy payment system provides a structural solution for this user group. By receiving payments through BenFen Chain, freelancers and small businesses can safely complete on-chain transactions while concealing transaction amounts, counterparty identities, and transaction correlations. This effectively prevents external parties from inferring pricing strategies or client relationships. The mechanism preserves the advantages of blockchain payments—efficiency, low cost, and global accessibility—while providing confidentiality comparable to traditional commercial systems. For the first time, participants in the individual economy gain a technical “negotiation protection umbrella.” Independent designers, developers, content creators, and cross-border merchants can manage their commercial data security like large enterprises, continuing to create value in a market environment defined by fairness and respect.

4. Financial Self-Defense in Geopolitical Contexts: Using a Turkish Designer as an Example to Build an “Economic Lifeline”

Amid intertwined geopolitical and macroeconomic risks, blockchain is increasingly becoming a “financial self-defense tool” for people in certain regions. In countries such as Turkey and Argentina, which have experienced hyperinflation or strict capital controls, freelancers, small business owners, and ordinary savers often cannot safely preserve wealth or conduct cross-border payments through traditional banking systems. As a result, they turn to blockchain and stablecoins, with digital assets serving as an alternative lifeline to hedge against local currency devaluation and circumvent capital restrictions.

However, a new challenge arises: the transparency of blockchain exposes these users on-chain. For example, a resident in Turkey seeking to convert part of their income into a USD stablecoin to hedge against Lira depreciation would find that all transfers, asset balances, and conversion paths are visible on-chain, meaning their entire financial footprint is exposed to public view. This creates a dual threat: regulators could use these public records to determine violations of capital flow policies, triggering “freeze first, review later” procedures that lock assets; simultaneously, visible wealth makes users easy targets for malicious actors. This vulnerability, caused by transparency, undermines blockchain’s protective role in high-risk countries.

This has created a strong, practical demand: in regions with restricted capital and volatile currencies, people need not only decentralized stores of value but also a privacy-layer protection mechanism, a financial infrastructure that allows them to survive in untrusted environments. BenFen Chain’s privacy payment functionality addresses this need. When users save assets on-chain or conduct peer-to-peer transfers via BenPay C2C, their asset balances and counterparty information are concealed. This allows them to hedge against inflation using stablecoins while mitigating the risks of on-chain transparency. Research from Chainalysis shows that in high-inflation and politically unstable regions, retail cryptocurrency adoption rises significantly, underscoring the urgency of this trend. We believe privacy payments are not merely a technological innovation but a foundational financial human rights infrastructure. They provide individuals in distress with the last line of defense to protect their wealth and conduct free transactions, serving as an “economic escape pod” that enables survival and preserves dignity under extreme conditions.

5. Preserving the Purity of Charitable Giving: Using Anonymous Donations as an Example to Safeguard the Spirit of Philanthropy

In today’s charitable landscape, public figures, entrepreneurs, or ordinary altruistic individuals often face social pressure, moral expectations, or persistent donation requests once their contributions—especially large or sensitive ones—are made public. For example, an entrepreneur seeking to fund cutting-edge scientific research or fringe artistic projects may prefer not to have their name publicly associated with the donation, in order to avoid unnecessary commercial attention or public misinterpretation. This can distort the purity of the philanthropic act and even discourage potential donors. Individuals wishing to donate anonymously, as well as charitable organizations prioritizing donor privacy, require a payment method that ensures funds reach the intended recipients securely and verifiably, while fully protecting donor anonymity, allowing goodwill to flow freely. A strong example is Ethereum founder Vitalik Buterin’s anonymous donation to Ukraine via Tornado Cash, demonstrating that even industry leaders value privacy in certain philanthropic contexts.

BenFen Chain’s privacy payment system is an ideal tool for achieving this goal. Donors can contribute directly to a charity’s public address on-chain, with the process fully concealing both the donor’s wallet address and the exact donation amount. BenFen Chain can further collaborate with major philanthropic foundations to promote the establishment of “Charitable Privacy Payment” standards, reshaping the culture of giving. This encourages more genuine, heartfelt donations—particularly attracting those who wish to remain low-profile or avoid public recognition—allowing philanthropy to return to its most pure and free essence.

For Governments and Third Parties: Privacy Payments as the Next-Generation Regulatory Technology for “Precision Compliance”

1. Achieving “Auditable Privacy”: Using the Tornado Cash Case as an Example to Explore a New Paradigm for AML/CFT Compliance

As blockchain technology continues to evolve—particularly with enhanced anonymity and privacy protection features—traditional financial regulatory systems are facing unprecedented pressure. Regulators have historically relied on transaction traceability and identifiable entities to enforce AML/CFT obligations. However, strong privacy technologies, while safeguarding user data, also reduce regulators’ ability to access on-chain transaction information. This “technical blindness” forces regulators to rely on traditional measures such as territorial jurisdiction and sanctioned entities, making it difficult to distinguish between legitimate users and bad actors. Consequently, regulatory measures often take a “one-size-fits-all” approach, simultaneously suppressing illegal activities and restricting lawful private transactions.

The Tornado Cash incident is a typical example of this dilemma. In 2022, the U.S. Department of the Treasury sanctioned Tornado Cash, citing the mixer’s use by certain malicious actors for money laundering, including funds associated with North Korean hacker groups (U.S. Treasury, 2022). The case demonstrates that, without effective tools to manage anonymous transactions, regulators can only take indirect measures to control risk, unable to precisely identify legitimate versus illicit activity. This highlights a deep tension: privacy technologies designed to protect individual rights are at odds with public safety objectives under existing frameworks. Regulators urgently need technological solutions that allow efficient and precise identification and prevention of illegal activity without monitoring all lawful transactions or infringing on public privacy, moving from “blanket enforcement” toward precision governance.

In response, BenFen Chain proposes an innovative solution of “auditable privacy.” By embedding compliance capabilities into the protocol layer via complex multi-party computation (MPC), regulability becomes a core feature rather than a post hoc add-on. Specifically, regulators can verify the compliance of transactions (e.g., confirming that a transaction does not involve sanctioned addresses) without viewing transaction amounts or participant identities, representing a shift from traditional “data-based oversight” to a logic-based regulatory paradigm.

BenFen Chain’s architecture employs a dual-layer compliance design:

• Layer 1: KYC-Based Identity
  In partnership with compliance service providers, off-chain KYC verification is offered for enterprises and high-frequency users, generating verifiable credentials. This ensures participants’ legitimacy and provides a procedural foundation for AML/CFT, serving as a trust anchor for all advanced financial activities, especially corporate payments and payroll.
• Layer 2: Protocol-Level Auditable Privacy
  Once identity compliance is confirmed, MPC is combined with zero-knowledge proofs to achieve both transaction privacy and auditability. Regulators can verify compliance without exposing transaction amounts or counterparties, ensuring that the vast majority of legitimate transactions enjoy default privacy while providing regulators with precise governance tools.

This dual-layer architecture systematically resolves the core tension in regulation: regulators can effectively combat illicit activity, while enterprises and individuals can protect financial data and commercial secrets within a compliant framework. BenFen Chain thus provides key infrastructure for enterprise-scale blockchain financial applications, demonstrating that privacy and compliance are no longer mutually exclusive, and ushering in a new era of regulatory technology.

2. Enhancing Tax Audit Efficiency: Using a Mid-Sized Tech Company as an Example to Build a More Harmonious Tax Relationship

In traditional tax audits, authorities typically require companies to provide years of bank statements and detailed accounts to verify the accuracy and compliance of their tax filings. Take a mid-sized technology company as an example: during a routine audit, it must provide transaction records spanning multiple years, and the process can take months, severely disrupting R&D and operational workflows. More importantly, management constantly worries that core business secrets—such as client lists, partners, and pricing strategies—might be exposed during the prolonged audit. This reflects a common dilemma: ensuring tax fairness while minimizing disruption to normal business operations.

With the maturation of blockchain technology, companies have begun exploring on-chain transaction recording in daily operations to improve efficiency, transparency, and security. Platforms such as Ethereum and Hyperledger can enable real-time on-chain recording and automation of financial transactions, potentially simplifying tax audits. PwC has also proposed blockchain-based solutions to track corporate taxes and transactions, aiming to improve compliance and reduce manual audit burdens through on-chain data.

However, existing blockchain solutions have significant limitations. While transparent ledgers provide a complete transaction record, every transaction’s amount, counterparties, and relational links are publicly visible. This exposes sensitive corporate data—particularly client lists, revenue structures, and partner information—meaning that standard blockchain records alone cannot protect privacy during audits.

BenFen Chain’s privacy payment and related technologies have emerged, aiming to fill this gap. By recording transactions on-chain, companies can generate necessary proofs for auditors without disclosing each transaction’s details. This “data usable but not visible” design ensures audit compliance while protecting corporate secrets and client privacy. Furthermore, BenFen Chain integrates advanced MPC at the protocol layer and combines it with off-chain KYC verification to form a dual-layer compliance system. Regulators can verify transaction compliance without viewing specific amounts or counterparties, achieving “default privacy with selective disclosure” at a high standard. This approach not only enhances audit efficiency and reduces corporate compliance costs but also fosters a more trustworthy and efficient tax relationship, paving the way for large-scale adoption of blockchain in corporate financial management and tax compliance.

The twelve scenarios we have analyzed collectively reinforce a key insight: privacy payments are not a peripheral feature but a core component that fixes critical flaws in existing blockchain paradigms and unlocks their true potential. By providing granular technical guarantees for corporate secrets, individual dignity, and regulatory efficiency, privacy payments enable blockchain technology to better serve mainstream businesses, individual users, and regulatory systems.

Yet this is just the beginning. Once payment privacy becomes a reliable foundational capability, a far broader space for innovation opens. Imagine:

• If institutions could execute strategies on-chain without being front-run, how much would DeFi liquidity grow?
• If DAO votes were no longer dominated by whales, how fair would governance become? (e.g., A16Z holding veto power in Uniswap)
• If auctions were no longer anchored by “first bids,” how accurate would price discovery be?
• If enterprises could jointly analyze data without revealing business secrets, how many new collaboration models could emerge?

These scenarios were previously impossible, not because the technology was immature, but because the tension between transparency and privacy remained unresolved. When forced to choose between “public for trust” and “private but isolated,” many high-value collaborations simply could not occur. Now, consider what can be built when privacy becomes infrastructure rather than a luxury.

This is not mere technological showmanship—it is a redefinition of trust: from “trust through transparency” to “trust through cryptography,” from “collaboration through visibility” to “collaboration within privacy.”

Building the Future Dream — From “Privacy Payments” to “Privacy Collaboration”: A Paradigm Leap

Over the past decade, we have witnessed breakthroughs in privacy technologies within the payments domain—protocols like Zcash, Monero, and Tornado Cash have made it impossible to trace who sent how much to whom. This marks the 1.0 era of privacy technology: information hiding.

But payment privacy is only the beginning. The real future lies in flow privacy, behavioral privacy, and ultimately collaborative privacy.

What distinguishes these three?

• Flow Privacy: Concealing transaction strategies, market behaviors, and intention patterns.
• Behavioral Privacy: Hiding operational behaviors, strategic paths, and market intentions to prevent inference of action patterns.
• Collaborative Privacy: Establishing a protected collaborative space among multiple parties, where data never leaves its local environment, yet insights are shared in encrypted form.

We believe the future world will revolve around “protected collaboration”, redefining the boundaries of payments, trading, governance, and social cooperation.

Privacy is no longer about being “invisible”—it is about being “selectively visible.”Trust is no longer derived from a central authority; it comes from verifiable cryptographic collaboration.

This is not a minor technical upgrade; it is a reconstruction of the very infrastructure of trust.

1. Transaction Flow Privacy: The Emergence of On-Chain Dark Pools

Why Do Institutions Need Privacy Markets?

The transparency of traditional blockchains is a blessing for retail investors but a curse for institutions.

When an asset management firm executes a large on-chain trade, the entire market can see it: counterparties may infer their strategy, arbitrageurs can front-run orders, and competitors can replicate their models. This “forced transparency” creates severe information asymmetry in on-chain markets, often distorting price discovery.

In traditional finance, dark pools exist to address this problem—allowing large trades to occur anonymously and minimizing market impact. However, centralized dark pools have fatal flaws:

• Operator risk: trades can be front-run, information leaked, or prices manipulated.
• Regulatory opacity: no way to verify that trades are executed fairly.
• Single-point-of-failure: the collapse of a centralized system can devastate the entire market.

TX-SHIELD is building a regulated dark pool on-chain, a trading infrastructure that enables selective transparency in a decentralized environment. BenFen Chain serves as the tangible implementation and core backbone of this infrastructure.

How Does Technology Enable “Selective Transparency”?

The core challenge here is: how can trading parties see each other, allow regulators to intervene when necessary, yet keep the rest of the market completely unaware?

TX-SHIELD implements a multi-layered privacy architecture:

1. Order Layer: An MPC-based order matching mechanism allows trading intentions to be submitted and matched in encrypted form.
2. Execution Layer: Zero-Knowledge Proofs (ZKP) ensure the validity of trades can be verified without revealing parameters such as price, quantity, or counterparty identity.
3. Compliance Layer: A Selective Disclosure mechanism enables regulators to hold decryption keys and access specific transaction records under legal procedures.

This design means:

• The market is no longer manipulable due to public visibility—trading intentions remain confidential until execution.
• Institutions can safely execute strategies on-chain without fear of information leakage.
• Stablecoins and RWA (real-world assets) can circulate privately yet compliantly, achieving both privacy and regulatory compliance.

Dark Pool is not merely a market tool—it is the foundational infrastructure of privacy finance. For the first time, traditional financial institutions can seriously consider going on-chain, without having to choose between transparency and strategic protection.

2. DAO Governance: How Confidential Voting is Reshaping DAOs

The Dilemma of DAO Governance: The Cost of Transparency

The ideal of Decentralized Autonomous Organizations (DAOs) is to enable community members to make collective decisions through voting, replacing traditional hierarchies with code and consensus.

In reality, DAO governance often falls short:

• Early visibility of votes: Large holders’ votes can influence smaller participants, creating a “herding effect” (e.g., Uniswap, Radiant Capital).
• Social influence hijacking rationality: Public positions of prominent KOLs can suppress dissenting opinions.
• Vote buying and collusion: When voting results are visible in real-time, coordinated attacks become easy.

The root cause of these problems is over-transparency, which creates information asymmetry rather than resolving it.

True democratic governance requires two conditions: freedom of expression (uninfluenced by others) and verifiable results (ensuring no cheating). Traditional public voting only satisfies the second condition.

Confidential Voting: Restoring Integrity to Governance

TX-SHIELD’s confidential voting mechanism is built on Homomorphic Encryption and Multi-Party Computation (MPC):

• Votes are submitted on-chain in encrypted form.
• Counting is performed in an encrypted state, so no one can see individual ballots.
• The final result is publicly verified via Zero-Knowledge Proofs (ZKP), ensuring the correctness of the tally.

While this may seem simple, it fundamentally redefines the trust logic of DAOs: “Privacy makes governance honest.”

Within this framework:

• Every vote is independent and uninfluenced by others.
• Large holders cannot manipulate smaller participants through “signal exhibition”.
• The correctness of outcomes can be mathematically verified, rather than relying on trust.

More importantly, this mechanism can be extended to more complex governance scenarios:

• Tiered governance: Votes of different weights are aggregated in encrypted form.
• Delegated voting: Delegation relationships remain private, while results remain traceable.
• Prediction markets: Fully decentralized prediction markets built on confidential voting.

When governance shifts from “public transparency” to “verifiable privacy”, DAOs truly gain the potential to become a new organizational paradigm.

3. Confidential Auctions: Unlocking True Value

What is the Essence of Auctions?

In economics, auctions are considered a “price discovery mechanism”—allowing competitive forces to reveal the true value of an item.

However, traditional auctions have a fundamental flaw: the Anchoring Effect. When the first bidder offers $1 million, others’ expectations are anchored around that figure. Even if someone believes the item is worth $2 million, they might only bid $1.1 million—afraid of “overpaying” and appearing foolish.

The result is that auctions often guide prices rather than discover them. The first bid shapes the entire market’s expectations.

Blockchain Implementation of Sealed-Bid Auctions

TX-SHIELD’s privacy auction mechanism adopts a digital sealed-bid format:

1. Submission Phase: Bidders submit encrypted bids, invisible to anyone—including the auctioneer.
2. Reveal Phase: All bids are automatically decrypted by the smart contract after the predetermined time.
3. Settlement Phase: The highest bidder wins, and other bids are returned (or, depending on design, a second-price auction mechanism is applied).

For the first time, this enables truly “symmetric information competition”—each bidder acts based on their true valuation rather than being influenced by others’ signals.

Applications: From NFTs to Carbon Credits

This mechanism can be applied wherever “fair price discovery” is required:

• NFT Auctions: The value of artworks is determined by true demand rather than speculative hype.
• Carbon Credit Markets: Companies bid according to actual emission reduction costs, not strategic pricing.
• Spectrum Auctions: Governments selling spectrum resources prevent operators from suppressing prices through signaling games.
• Data Marketplaces: Enterprises bid for datasets under privacy protection, avoiding exposure of commercial strategy.

Confidential auctions are more than a technical tool—they are a critical step for privacy technologies to enter the value discovery layer. They demonstrate that privacy is not an enemy of efficiency, but a prerequisite for a fair market.

4. Unlocking New Modes of Collaboration

The Ultimate Goal of Privacy: Reshaping Social Collaboration

If the previous three scenarios illustrate privacy technologies applied at the transaction and governance layers, the fourth dimension focuses on reconstructing social collaboration itself.

When individuals, institutions, and machines can securely collaborate in encrypted states, we unlock new socio-economic models—models that were previously impossible due to “high trust costs” or “insufficient privacy guarantees”.

Scenario 1: Anonymous Creative Reward Systems

Problem: Creators often face “identity bias”—works by well-known authors attract attention, while newcomers’ works are overlooked. This distorts evaluation systems.

TX-SHIELD Solution:

• Submissions are encrypted, hiding authors’ identities.
• Reviews and tipping occur anonymously.
• Once a work reaches a quality threshold, the creator may choose to reveal their identity.
• Revenue sharing is automatically executed via smart contracts, distributing earnings proportionally among creators, curators, and platforms.

This approach has already shown potential in music, literature, and design. When “work” is decoupled from “author identity”, creative value can be assessed purely.

Scenario 2: Decentralized Credit Lending

Problem: Traditional credit evaluation relies on centralized institutions (banks, credit bureaus) that either cannot cover global users or pose data monopoly and privacy abuse risks.

TX-SHIELD Solution:

• Users’ on-chain behavior (transaction history, DeFi participation, social reputation) is aggregated in encrypted form into a “credit score.”
• Lenders and borrowers can verify each other’s creditworthiness without exposing underlying data.
• Scoring models are community-governed: algorithms are transparent, data remains private.

This “encrypted credit assessment without revealing identity” enables the unbanked to access financial services while safeguarding privacy.

Scenario 3: Cross-Enterprise Collaborative Data Sharing

Problem: Industries such as healthcare, finance, and logistics possess high-value data, but privacy regulations (GDPR, HIPAA) and competitive concerns prevent sharing. Consequently, AI model training is limited, and industry insights remain inaccessible.

TX-SHIELD Solution:

• Using Federated Learning (FL) + Multi-Party Computation (MPC), data remains local to each enterprise.
• Model training occurs in encrypted form, with only differentially private gradients shared.
• The final model is jointly owned, but no party can access another’s raw data.

This model is transforming fields such as medical research (joint training of diagnostic models by multiple hospitals), financial risk control (joint anti-fraud by multiple banks), and supply chain optimization (joint demand prediction by multiple enterprises).

This is precisely the vision of TX-SHIELD’s MPC-FL(Multi-Party Secure Computing Federated Learning) framework: a system that enables “privacy to become the infrastructure for social collaboration”. More importantly, TX-SHIELD resolves the core conundrum in federated learning: how to fairly quantify the contributions of each party? This is the essential difference between TX-SHIELD and traditional federated learning solutions.

Traditional federated learning allows all participants to train a model equally, without measuring individual contributions. This creates:

• Free-rider problem: low-quality contributors benefit equally.
• Misaligned incentives: high-value contributors lack motivation to continue.

TX-SHIELD’s framework quantifies each participant’s contribution in encrypted form, without exposing raw data:

• Enterprise A contributed 30%—its data improved model accuracy marginally.
• Enterprise B contributed 34%—its data covered critical long-tail scenarios.
• Enterprise C contributed 36%—its high-quality data reduced model variance.

Based on these contributions, rights and rewards are allocated automatically:

• Governance rights: voting power proportional to contribution; high contributors influence model iteration decisions.
• Revenue rights: commercial revenue shared according to contribution.
• Data sovereignty: participants can exit at any time; contributions are recorded, raw data remains private.

This contribution is not calculated once but is dynamically updated as the model iterates. When one party continuously provides high-quality data, its equity share will gradually increase. If the data quality declines or contributions cease, the proportion will decrease accordingly. The distribution of rights will only be solidified when the model is no longer updated. This forms a self-optimizing incentive mechanism – participants have the motivation to continuously provide high-quality data rather than making a one-off deal.

This is the essential difference between TX-SHIELD and traditional federated learning solutions.

Existing solutions (Google Federated Learning (FL), OpenMined, etc.) solve “privacy-preserving model training”, but assume equal participation: whether a participant provides 1 million high-quality records or 10,000 low-quality ones, rights remain the same.

TX-SHIELD further answered: How can the fairness of collaboration be ensured under privacy protection? We not only protect privacy, but also quantify contributions and distribute rights and interests.

This “verifiable fairness” transforms collaboration from a matter of moral constraint into one of mechanism-based guarantee:

In healthcare, large tertiary hospitals and community clinics can collaborate equitably—tertiary hospitals provide complex case data, while community clinics provide common-disease data. The contributions of both parties are accurately measured, eliminating the previous dynamic of “large hospitals lead, small clinics follow.”

In finance, major banks and small fintech companies can jointly combat fraud—the historical data from large banks and real-time data from smaller companies are both valuable, with rights and benefits distributed according to actual contribution, breaking the zero-sum game of “big eats small.”

In supply chains, brands, logistics providers, and retailers can jointly optimize inventory—each party’s data (sales forecasts, transport efficiency, inventory turnover) is quantified into specific contributions to model improvement, and gains are shared proportionally.

This is not merely a technological innovation; it is a revolution in collaboration paradigms: when contributions can be quantified, trust becomes computable; when rights and benefits can be verified, collaboration can occur and be sustained.

Scenario 4: Confidential Collaboration Infrastructure and Data Quantification

The most radical vision is this: the intelligence of the future will not belong to a single AI, but to networks of AI. Multiple AI agents will collaborate within a trusted privacy layer, much like neurons form a brain—individual neurons are ordinary, but the network of connections creates consciousness.

Why do AIs need to collaborate? Today’s AI models are increasingly specialized: some excel at image recognition, others at natural language, and some focus on mathematical reasoning. Yet real-world problems often require cross-domain capabilities—diagnosing diseases requires analyzing medical images, clinical notes, and genetic data; autonomous driving requires integrating vision, path planning, and traffic prediction.

A single AI cannot do everything; collaboration becomes inevitable.

However, there is a fundamental tension: AI models are assets and sources of competitive advantage. When two AIs need to collaborate, they cannot simply “reveal themselves” to each other—doing so risks reverse-engineering models, inferring training data, and leaking trade secrets.

TX-SHIELD’s core solution is to provide the cryptographic infrastructure for AI collaboration.

Specific Scenarios:

Medical Diagnosis Collaboration

• Agent A (Imaging AI) analyzes CT scans and identifies lung anomalies
• Agent B (Pathology AI) infers potential causes from symptom descriptions
• Agent C (Genomics AI) evaluates treatment options based on patient genotypes
• The three AIs exchange inference results in a confidential manner to generate a comprehensive diagnosis
• No AI can see another AI’s model parameters or training data

Financial Risk Collaboration

• Agent A (Trading AI) detects abnormal trading patterns
• Agent B (Credit AI) evaluates historical credit records
• Agent C (Fraud AI) cross-verifies multi-source data
• The final risk score is output, while each AI’s model and data remain isolated

Autonomous Driving Collaboration

• Vehicle AI collaborates with City Traffic AI, Weather Forecast AI, and Logistics Dispatch AI
• Necessary information (road conditions, weather, delivery requirements) is shared in a confidential manner
• Each AI’s algorithmic logic, historical trajectory data, and commercial strategies remain private

Technical Implementation: Confidential Model Inference Collaboration When AI agents need to share models, experience, or inference results, they should not directly expose data to each other (which risks reverse-engineering or data leakage). Instead, information is exchanged in a confidential manner:

• Agent A and Agent B jointly derive a conclusion without exposing their models
• Agent C can verify the conclusion’s correctness but cannot reverse-engineer A or B’s model parameters
• The collaboration is auditable, while inference details remain private

This “confidential collaboration” will become the foundation of the future AI economy.

As AI agents begin to own assets (cryptowallets, digital identities), execute contracts (on-chain smart contracts), and provide services (API calls, data exchanges), the trust mechanism between them must be cryptographic—not “I trust you won’t cheat,” but “cryptography guarantees you cannot cheat.”

Furthermore, TX-SHIELD’s contribution quantification mechanism can also be applied to AI collaboration: each agent’s contribution to the final result is measurable, and revenue is distributed proportionally. This creates true “economic collaboration” among AIs rather than mere technical integration.

Imagine a future Web3 collaboration scenario based on TX-SHIELD:

• A medical diagnosis task is completed by five specialized AI agents, charging $100
• Imaging AI contributes 35%, Pathology AI 30%, Genomics AI 20%, Pharmacology AI 10%, Coordination AI 5%
• Revenue is automatically distributed: $35/$30/$20/$10/$5
• The entire process is verifiable on-chain, while each AI’s models and data remain fully private

This is not science fiction; it is the inevitable convergence of cryptography, blockchain, and AI.

TX-SHIELD aims to be the trust infrastructure for this AI collaboration network—enabling agentic AIs or robots to collaborate like humans, but more reliably. In this new paradigm, trust comes from cryptography. You do not need to reveal information, only prove that you follow the rules. Zero-knowledge proofs, multi-party computation, and federated learning decouple “verification” from “disclosure”, and collaboration from exposure. We believe privacy is not a boundary, it is a bridge to the future.

Conclusion: Privacy as the New Language of Trust

Starting from the need to “solve present-day pain points,” we have witnessed how privacy payments serve as a robust shield—protecting trade secrets, defending personal dignity, and enabling precise compliance. It addresses the inherent shortcomings of transparent blockchains, enabling them to genuinely serve real-world needs. Moving forward, we move towards the “dream of building the future”, envisioning the boundless possibilities that will arise when privacy becomes the default setting. From on-chain Dark Pool to confidential voting, from sealed-bid auctions to entirely new paradigms of data collaboration, it is clear that privacy is no longer just about “hiding”; it is about “empowerment”. It is not merely a defensive shield but also an engine for collaboration.

This represents a profound paradigm shift: we are transitioning from an era where “trust must be purchased with transparency” to a new epoch where “trust is guaranteed through cryptography”. Trust moves from forced public exposure to verifiable confidential computation.

The joint exploration of BenFen public chain and TX-SHIELD embodies this vision: we are not simply developing a feature or protocol; we are collectively laying the foundation for the next-generation Internet of trust. In the future:

• Enterprises can collaborate without fear in competitive environments, unlocking innovation;
• Individuals can live freely in the digital world, reclaiming their sovereignty;
• Society can achieve more efficient collaboration while safeguarding privacy, unleashing collective intelligence.

Privacy has never been an endpoint. It is the starting point toward a freer, fairer, and more efficient digital civilization, and we are working together to make it a reality.

About BenFen

BenFen Chain is a high-performance public blockchain specifically designed for stablecoin payments. Built on the Move language, it delivers a secure, low-cost, and highly scalable underlying network. A key feature is the ability for users to pay gas fees directly with stablecoins, significantly lowering the barrier to entry and paving the way for large-scale adoption.

Beyond robust cross-chain and multi-asset settlement capabilities, BenFen provides rich ecosystem applications that cover diverse payment scenarios. Critically, it offers enterprise-grade privacy payment options, ensuring that businesses can leverage blockchain efficiency without exposing sensitive commercial data.

BenFen aims to become a global stablecoin circulation network serving payroll, cross-border payments, e-commerce, and offline merchants, a next-generation financial infrastructure balancing efficiency, cost, and security

About TX-SHIELD

TX-SHIELD is a regulatable on-chain privacy settlement infrastructure, providing private yet auditably compliant payment and settlement capabilities for stablecoins and blockchain applications.

Core Solution:

• TX-SHIELD: A privacy infrastructure for blockchain applications, enabling confidential transactions, Dark Pools, and privacy-centric protocol layers.

Our Innovation:We go beyond protecting transaction privacy; through distributed cryptography, TX-SHIELD reshapes asset ownership and security. Its solution allows enterprises and financial institutions to engage in joint custody, confidential settlement, and auditable compliance without revealing sensitive business information.

We are building an infrastructure where privacy is no longer a barrier to regulatory adoption or institutional use, but a protective layer for financial flows.

TX-SHIELD — The private and regulatable settlement base-layer for stablecoin, blockchain, and enterprise.