Huobi Growth Academy | In-Depth Research Report on Privacy Coins: The Paradigm Shift from Anonymous Assets to Compliant Privacy Infrastructure
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Huobi Growth Academy | In-Depth Research Report on Privacy Coins: The Paradigm Shift from Anonymous Assets to Compliant Privacy Infrastructure
The core dividing line in the privacy赛道 is no longer "whether privacy," but "how to use privacy under compliance前提."
Navigating Web3 tides with focused insightsAbstract
As institutional capital continues to grow in the cryptocurrency market, privacy is shifting from a niche demand for anonymity into a critical infrastructure capability for blockchain’s integration into real-world financial systems. Blockchain’s public transparency was once considered its core value proposition, but as institutional participation becomes dominant, this feature reveals structural limitations. For enterprises and financial institutions, full exposure of transaction relationships, position structures, and strategic timing inherently poses significant commercial risks. Thus, privacy is no longer an ideological choice but a necessary condition for blockchain to achieve scale and institutional adoption. Competition within the privacy sector is also shifting—from “degree of anonymity” toward “institutional compatibility.”
1. The Institutional Ceiling of Full Anonymity: Monero's Strengths and Challenges
The fully anonymous privacy model represented by Monero constitutes the earliest and most “pure” technical approach in the privacy space. Its core objective is not to balance transparency with privacy, but to minimize on-chain observability—making it as difficult as possible for third parties to extract meaningful transaction data from the public ledger. Toward this end, Monero employs ring signatures, stealth addresses, and confidential transactions (RingCT) to obscure all three key elements: sender, receiver, and amount. External observers can confirm that “a transaction occurred,” but cannot reliably reconstruct its path, counterparties, or value. For individual users, this experience of “default privacy, unconditional privacy” is highly attractive—it turns privacy from an optional feature into a system-wide norm, significantly reducing the risk of long-term tracking via data analytics tools, and offering near-cash levels of anonymity and unlinkability in payments, transfers, and asset holding.
At the technical level, the value of full anonymity lies not just in “hiding,” but in its systemic resistance to on-chain analysis. The greatest externality of transparent chains is “composable surveillance”: publicly available transaction data can be continuously pieced together through address clustering, behavioral pattern recognition, and off-chain data cross-referencing, eventually linking digital activity to real-world identities and forming monetizable, exploitable “financial profiles.” Monero raises the cost of such attribution to a point where behavior changes—when large-scale, low-cost analysis becomes unreliable, both surveillance deterrence and fraud feasibility decline. In other words, Monero does not serve only those who wish to “do bad things”; it responds to a more fundamental reality: in a digital environment, privacy itself is part of security.
However, the fundamental issue with full anonymity is that it is irreversible and non-conditional. For financial institutions, transaction data are not merely essential for internal risk control and auditing—they are legally mandated under regulatory frameworks. Institutions must retain traceable, explainable, and submittable evidence trails to comply with KYC/AML requirements, sanctions compliance, counterparty risk management, anti-fraud measures, taxation, and accounting audits. Fully anonymous systems permanently lock these data at the protocol layer, making compliance structurally impossible—even if institutions are willing to comply, they lack the means. When regulators require explanations of fund origins, proof of counterparty identity, or details about transaction amounts and purposes, institutions cannot reconstruct key information from the chain nor provide verifiable disclosures to third parties. This is not a case of “regulators failing to understand technology,” but rather a direct clash between institutional objectives and technical design: the foundation of modern finance is “auditable when necessary,” while full anonymity stands on “never auditable under any circumstances.”
This conflict manifests externally as systematic rejection of strongly anonymous assets by mainstream financial infrastructure: delistings from exchanges, lack of support from payment and custody providers, and barriers preventing compliant capital from entering. Notably, this does not mean demand disappears. Instead, demand often migrates to more opaque, higher-friction channels, fueling the growth of “compliance vacuums” and “gray intermediaries.” In Monero’s case, instant exchange services have at times absorbed substantial buying and swapping demand, with users paying higher spreads and fees while bearing risks of frozen funds, counterparty exposure, and informational opacity. More critically, these intermediaries’ business models may introduce persistent structural selling pressure: when service providers quickly convert collected Monero fees into stablecoins and cash out, the market experiences continuous passive sell-side pressure unrelated to genuine buy-side demand, thereby suppressing price discovery over time. A paradox emerges: the more excluded from compliant channels, the more demand concentrates in high-friction intermediaries; the stronger these intermediaries become, the more distorted prices get; the more distorted prices are, the harder it becomes for mainstream capital to assess and enter via “normal market” mechanisms—creating a vicious cycle. This is not a sign of “market rejection of privacy,” but rather the outcome shaped jointly by institutional and channel structures.
Therefore, evaluating Monero’s model requires moving beyond moralistic debates and confronting the real constraint of institutional compatibility: full anonymity is “default secure” in the individual realm, but “default unusable” in the institutional world. The more extreme its advantages, the more rigid its limitations. Even if narratives around privacy gain momentum in the future, fully anonymous assets will likely remain confined primarily to non-institutional use cases and specific communities. In the era of institutional dominance, mainstream finance will lean instead toward “controllable anonymity” and “selective disclosure”—protecting commercial secrets and user privacy while enabling audit-ready, regulator-accessible evidence under authorized conditions. In short, Monero is not a technological failure, but one locked into a usage scenario that institutions struggle to accommodate. It proves strong anonymity is feasible from an engineering standpoint—and equally clearly demonstrates that in an era of regulated finance, the competitive focus of privacy shifts from “can we hide everything?” to “can we prove everything when needed?”
2. The Rise of Selective Privacy
Against the backdrop of full anonymity hitting institutional ceilings, the privacy landscape is undergoing a directional shift. “Selective privacy” has emerged as a new technological and institutional compromise—not seeking to oppose transparency, but introducing a controllable, authorizable, and disclosable privacy layer atop otherwise verifiable ledgers. The underlying logic is transformative: privacy is no longer seen as an escape tool from regulation, but redefined as an infrastructural capability that can be absorbed by institutions. Zcash is the most representative early example of this path. By supporting both transparent addresses (t-addresses) and shielded addresses (z-addresses), it gives users the freedom to choose between openness and privacy. When using shielded addresses, senders, receivers, and amounts are encrypted on-chain; when compliance or audit needs arise, users can disclose full transaction details to specific third parties via “viewing keys.” This architecture is conceptually groundbreaking—it was among the first major privacy projects to assert that privacy need not come at the cost of verifiability, and that compliance doesn’t require complete transparency.
From an institutional evolution perspective, Zcash’s significance lies less in adoption rates than in its role as a “proof of concept.” It demonstrated that privacy could be optional rather than default, and that cryptographic tools could preserve technical interfaces for regulatory disclosure—a crucial point given current regulatory climates. Major jurisdictions globally do not reject privacy per se, but oppose “non-auditable anonymity.” Zcash’s design directly addresses this concern. However, as selective privacy moves from “individual transfer tool” to “institutional transaction infrastructure,” Zcash’s structural limitations become apparent. Its privacy model remains fundamentally binary at the transaction level: each transaction is either fully public or entirely hidden. This dichotomy is too coarse for real-world financial applications. Institutional transactions involve multiple participants and layered responsibilities—counterparties need to verify execution terms, clearing and settlement entities require precise amounts and timing, auditors must validate full records, and regulators may only care about source-of-funds and compliance attributes. These stakeholders have asymmetric, non-overlapping information needs.
In such contexts, Zcash cannot decompose transaction data into modular components or enable differentiated authorization. Institutions cannot selectively disclose only “necessary information”; they must choose between full disclosure and full concealment. As a result, in complex financial workflows, Zcash either exposes excessive commercially sensitive data or fails to meet basic compliance demands. Its privacy capabilities thus struggle to integrate into actual institutional operations, remaining marginal or experimental. In contrast, Canton Network represents a different paradigm of selective privacy. Rather than starting from “anonymous assets,” Canton begins with the operational processes and institutional constraints of financial firms. Its core idea is not to “hide transactions,” but to “manage information access rights.” Using the smart contract language Daml, Canton breaks down transactions into multiple logical components, ensuring that different participants see only the data fragments relevant to their permissions—all other information is isolated at the protocol layer. This shift is foundational: privacy ceases to be an after-the-fact attribute and becomes embedded in contract structure and permission systems, forming an integral part of compliance workflows.
From a broader perspective, the contrast between Zcash and Canton reveals diverging trajectories in the privacy space. The former remains rooted in the crypto-native world, attempting to balance personal privacy with compliance. The latter actively embraces real-world finance, treating privacy as something to be engineered, proceduralized, and institutionalized. As institutional capital grows in the crypto market, so too will the battleground for privacy shift accordingly. The future competition will no longer center on who can hide best, but on who can operate at scale—being auditable, regulator-friendly, and usable—while exposing minimal unnecessary information. Under this standard, selective privacy is not merely a technical path, but a necessary route to mainstream finance.
3. Privacy 2.0: Infrastructure Upgrade from Transaction Hiding to Privacy-Preserving Computation
Once privacy is redefined as a prerequisite for institutional blockchain adoption, the technological scope and value proposition of the privacy sector expand accordingly. Privacy is no longer understood simply as “whether transactions are visible,” but evolves toward deeper questions: can systems perform computation, collaboration, and decision-making without exposing the underlying data? This marks a transition from Privacy 1.0—focused on “private assets” and “private transfers”—to Privacy 2.0, centered on privacy-preserving computation, where privacy upgrades from an optional feature to a universal infrastructure layer.
In the Privacy 1.0 era, technical focus centered on “what to hide” and “how to hide it”—obscuring transaction paths, amounts, and identity links. In the Privacy 2.0 era, attention shifts to “what else can be done while remaining hidden.” This distinction is crucial. Institutions don’t just need private transfers—they need to execute complex operations like trade matching, risk modeling, clearing and settlement, strategy execution, and data analysis—all under privacy guarantees. If privacy applies only at the payment layer but not at the business logic layer, its utility for institutions remains limited.
Aztec Network represents one of the earliest forms of this shift within blockchain systems. Aztec does not treat privacy as a tool to resist transparency, but embeds it as a programmable property within the execution environment of smart contracts. Through a zero-knowledge-proof-based Rollup architecture, Aztec allows developers to precisely define which states are private and which are public at the contract level, enabling hybrid logic of “partial privacy, partial transparency.” This capability extends privacy beyond simple transfers to cover complex financial constructs like lending, trading, vault management, and DAO governance.
Yet Privacy 2.0 does not stop at blockchain-native ecosystems. With the rise of AI, data-intensive finance, and cross-institutional collaboration, relying solely on on-chain zero-knowledge proofs is insufficient. Hence, the privacy sector is evolving toward broader “privacy computing networks.” Projects like Nillion and Arcium emerge in this context. Their shared characteristic is not replacing blockchains, but serving as privacy-preserving collaboration layers between blockchains and real-world applications. By combining multi-party computation (MPC), fully homomorphic encryption (FHE), and zero-knowledge proofs (ZKP), these systems allow data to be stored, accessed, and computed upon while remaining fully encrypted. Participants can jointly perform model inference, risk assessment, or strategy execution without ever accessing raw data. This elevates privacy from a “transaction-layer attribute” to a “computation-layer capability,” expanding its potential market into areas like AI inference, institutional dark pools, RWA data disclosure, and inter-enterprise data collaboration.
Compared to traditional privacy coins, the value proposition of privacy computing projects has shifted significantly. They do not rely on “privacy premium” as a core narrative, but on functional irreplaceability. When certain computations cannot be performed in open environments—or would lead to severe commercial risks or security breaches in plaintext—the question is no longer “do we need privacy?” but “can we function without it?” This gives the privacy sector its first real chance at building a “foundational moat”: once data, models, and workflows are embedded within a privacy computing network, migration costs far exceed those of typical DeFi protocols. Another hallmark of Privacy 2.0 is the engineering, modularization, and invisibility of privacy. It no longer exists as explicit “privacy coins” or “privacy protocols,” but is broken down into reusable modules integrated into wallets, account abstraction layers, Layer2 solutions, cross-chain bridges, and enterprise systems. End users may not even realize they are “using privacy,” yet their balances, strategies, identity linkages, and behavioral patterns are protected by default. This “invisible privacy” better aligns with the practical path to mass adoption.
Regulatory focus also shifts accordingly. In the Privacy 1.0 phase, the central question was “does anonymity exist?” In Privacy 2.0, it becomes “can compliance be verified without revealing raw data?” Zero-knowledge proofs, verifiable computation, and rule-level compliance thus become the key interface through which privacy computing projects engage with institutional frameworks. Privacy is no longer viewed as a source of risk, but redefined as a technical enabler of compliance. Collectively, Privacy 2.0 is not a simple upgrade of privacy coins, but a systemic response to the question: “How can blockchain integrate into the real economy?” It signifies that competition in the privacy space is moving from the asset layer to the execution layer, from the payment layer to the computation layer, and from ideology to engineering capability. In the institutional era, the privacy projects with lasting value may not be the most “mysterious,” but the most “usable.” Privacy computing embodies this logic most clearly at the technical level.
4. Conclusion
In summary, the key dividing line in the privacy sector is no longer “whether there is privacy,” but “how privacy can be used under compliance constraints.” Fully anonymous models offer irreplaceable security benefits at the individual level, but their inherent non-auditability prevents them from supporting institutional-grade financial activity. Selective privacy, through designs enabling disclosure and authorization, provides a viable technical bridge between privacy and regulation. The emergence of Privacy 2.0 further elevates privacy from an asset feature to an infrastructure capability for computation and collaboration. Going forward, privacy will cease to exist as an explicit function, becoming instead a default assumption embedded across financial and data workflows. The projects with true long-term value may not be the most “secretive,” but the most “usable, verifiable, and compliant.” This marks the critical transition of the privacy sector from experimentation to maturity.
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