Privacy × Compliance: A New Web3 Narrative?

Original Author: Jesse, Researcher at Web3Caff Research

Over the past two years, a significant shift has been underway: the binding of public chain transparent ledgers to real-world identities is progressing far faster than the industry anticipated. On-chain analysis tools and compliance services are maturing rapidly, with capabilities such as Travel Rule, on-chain risk control, address profiling, and entity correlation being deployed at scale. This makes on-chain behavior increasingly easy to map to individuals, enterprises, and institutions in the real world. Concurrently, stablecoin cross-border settlements, the tokenization of RWA assets, and institutional experimentation with on-chain clearing have elevated the question of "who can see what, and under what conditions" from a technical detail to a core issue within compliance frameworks. Compounded by multiple trust crises and real-world security incidents triggered by transaction traceability, a previously overlooked fact is becoming clear: in the on-chain world, visibility itself is becoming a risk exposure. These changes collectively point to one reality: privacy is no longer a question of "whether it is needed," but rather "how to redesign the structure of visibility under the premise of compliance and verifiability."

On a deeper level, privacy is a self-protection mechanism for the individual: it allows individuals to make decisions without having every single action subjected to the immediate evaluation of others, institutions, or automated systems. Because when all behavior is public by default, decision-making logic inevitably tilts towards "external visibility." Individuals and organizations are forced to continuously optimize "how others interpret my actions," thereby weakening the stability and autonomy of long-term goals.

This structural pressure is significantly amplified in scenarios such as on-chain finance, DAO governance, and enterprise-level on-chain decision-making. When capital flows, voting behaviors, and strategic adjustments are all in a state of being continuously traceable and analyzable, transparency itself can inversely alter participants' behavioral patterns, thereby affecting the overall efficiency and game-theoretic outcomes of the system. [1]

It is noteworthy that the Web3 industry has not always prioritized privacy. On the contrary, for a considerable period, privacy has been undervalued: before technologies like ZK-SNARKs matured, achieving strong privacy under decentralized conditions was nearly impossible. The industry narrative naturally gave way to more "deliverable" goals, such as scalability, decentralization, governance, and composability. However, this avoidance strategy began to fail around 2024–2025. On one hand, AI has pushed the capabilities of "centralized data collection + high-intensity analysis" to historical highs, even extending to user behavior patterns not actively disclosed. On the other hand, cryptography itself is evolving. Zero-knowledge proofs, fully homomorphic encryption, and secure multi-party computation are beginning to achieve engineering feasibility, making "programmable privacy" a realistic option and unlocking the value of data.

This reveals a long-overlooked tension: while transparency is indeed a key attraction of cryptographic systems, excessive transparency can undermine the order upon which the real world operates. Corporate procurement and trading strategies, individual asset allocation and consumption habits, institutional fund allocation and risk exposures—these cannot stably exist in a fully observable environment. The very reason real-world societies can coordinate complex collaboration lies precisely in the fine-grained layering of visibility and verifiability, seen without exception in law, commerce, content governance, and organizational decision-making. Privacy is not anti-order; it is one of the prerequisites for order to exist.

It is precisely against this backdrop that the technical connotation of privacy has undergone a fundamental change. The key value of tools like ZK and FHE is no longer just "hiding information," but precisely controlling who can verify what and under what conditions: proving age without revealing identity, proving uniqueness without disclosing the individual, excluding non-compliant participants from a privacy pool without exposing the complete transaction graph. Such capabilities are reshaping the fundamental assumptions of finance, identity, governance, and AI interaction.

Consequently, a seemingly counterintuitive question emerges: Why was the core of the last ZK narrative focused on ZkRollup, ZkEVM, and ZkVM, while in this cycle, it is the "privacy infrastructure" that has been ignited first? From 2021–2023, zero-knowledge proofs were primarily viewed as scaling tools to increase throughput without sacrificing security. By 2024–2025, the convergence of compliance trends, on-chain surveillance, RWAs, and AI has made users realize: performance is no longer the sole scarce commodity; (compliant) privacy itself is becoming a luxury good.

This content is excerpted from the research report "Privacy Infrastructure Track 26,000-Word Research Report: How is Privacy Reshaping the Underlying Paradigm of Web3 Amid the Global Compliance Wave? Examining the Divergence of ZK / FHE / TEE Paths, Compliance Architecture Choices, Ecosystem Status, and Evolution Trends for the Next Decade from the Perspective of Four Generations of Privacy Technology Evolution" published by Web3Caff Research.

This report, authored by Web3Caff Research researcher Jesse, systematically outlines the evolutionary path of privacy technology within the Web3 ecosystem. It focuses on discussing the differences in system architecture, verifiability, and scenario adaptation among various privacy technology approaches against the backdrop of expanding on-chain finance scale and increasing application complexity. Its core points include:

• Industry Context Shift: High observability of on-chain activities is making privacy a structural issue at the system level.
• Technology Path Divergence: Evolution from anonymity and obfuscation schemes to trade-offs between different privacy architectures like ZK, FHE, and TEE.
• Core Capability Differences: The trade-offs between regulatory compliance, verifiability, performance cost, and engineering complexity across different approaches.
• Key Areas for Future Observation: The validation process of privacy infrastructure in practical deployment, ecosystem adoption, and long-term evolution.