What does "privacy" actually refer to in the blockchain network? Why is it difficult to achieve?

Author: Stacy Muur Translator: Shan Ouba, Golden Finance

Your crypto wallet is broadcasting your entire financial life to the world. Fortunately, new on-chain privacy technologies are truly allowing you to regain control of your data.

Fundamental Misunderstanding

Most discussions about blockchain privacy have seriously missed the point. Privacy is often simplified to being a tool for "dark web users" or directly equated with criminal activities. This narrative completely misunderstands the true meaning of privacy: privacy is not about "hiding oneself," but rather the ability to choose when, to whom, and what information to disclose.

Think from a different perspective: In real life, you wouldn't announce your bank account balance to every person you meet, wouldn't hand your medical records to the cashier, nor would you share your geographical location in real-time with all businesses. You would disclose information selectively based on the specific context, relationships, and needs. This is not antisocial behavior, but rather the foundation of normal interactions in human society.

However, in Web3, the systems we build make every transaction, every interaction, every preference public, accessible to anyone with an internet connection.

We mistake "radical transparency" for progress, while what we truly need is "radical control" – that is, allowing users to decide what they want to disclose and what they do not want to disclose.

Why Current Blockchain Privacy Fails

When public chains were designed, "complete transparency" was viewed as a feature rather than a flaw. In the early stages, the main goal of blockchain was to prove that "decentralized currency" is feasible, and thus making every transaction verifiable by everyone was a necessary condition for establishing the credibility of a "trustless system."

However, as blockchain applications expand from simple value transfer to more complex fields such as finance, identity, gaming, and artificial intelligence, this transparency has instead become a burden. Here are a few real-world examples:

• Financial Privacy: Your trading strategies, asset allocations, and sources of income are exposed to competitors, employers, and even malicious attackers;
• Identity Exposure: Participating in governance indicates your political stance; using DeFi reveals your economic behavior; playing blockchain games links your entertainment preferences with your wallet address;
• Strategic Vulnerability: The DAO cannot discuss issues privately; the company cannot develop new products under confidentiality; individuals cannot safely experiment, as everything will be permanently recorded and may affect their reputation.

This situation is not only uncomfortable but also brings about a decline in economic efficiency. The market can function better when participants cannot preemptively understand each other's strategies. Governance can work more effectively when votes cannot be bought or coerced. Innovation can occur more rapidly when experiments do not incur permanent reputational costs.

Privacy Stack Layer

Before diving deeper, let's first take a look at the specific role of privacy features within the blockchain technology stack:

• Application Layer: User-side privacy features such as wallet privacy protection, encrypted messaging, etc.
• Execution Layer: Private smart contracts, confidential state transitions, encrypted computation
• Consensus Layer: Privacy-preserving verification mechanism, encrypted block generation process
• Network Layer: Anonymous communication, metadata protection, anti-traffic analysis technology

Currently, most privacy solutions remain at the "application layer", which is why they appear to be "external patches" rather than native features of blockchain systems. To achieve true privacy protection, integration must occur at all levels.

PET: Privacy-Enhancing Technology

Privacy-Enhancing Technologies (PETs) are the foundation of cryptography and system architecture for building selective information disclosure and confidential computing capabilities. Rather than treating privacy as an optional "plugin," PETs tend to embed privacy into the system design.

We can categorize PET into three functional categories:

1. Proof and Certification: Proving a certain characteristic or state of the data without disclosing the original data.
2. Private Computation: Completing computation tasks while keeping data confidential.
3. Metadata and Communication Privacy: Hiding the identity, time, and objects of traders.

Zero-Knowledge Proofs (ZKPs)

Zero-Knowledge Proofs (ZKPs) are a cryptographic method that allows a prover to demonstrate the validity of a statement to a verifier without revealing the specific data content. The application of ZKPs in blockchain is extremely broad, including but not limited to:

• Private Transactions (such as zk-rollups, Aztec, etc.)
• Identity Verification (e.g., ZK Email, Sismo, ZK Passport)
• Private Program Execution (e.g., Noir, zkVMs)

The main forms of ZKP include:

• zk-SNARKs: Proof size is small, verification speed is fast, but requires a trusted setup.
• zk-STARKs: No trusted setup required, resistant to quantum computing attacks, but the proof size is relatively large.
• Recursive Proof: Supports compressing complex computations into concise proofs.

ZKP is the core pillar of "programmable privacy": only the necessary information is disclosed, everything else is kept confidential.

Real-world application examples:

• Achieving Private Transactions under Auditability
• Complete identity verification without exposing personal identity
• Use confidential inputs and logical execution for private computing programs

Secure Multi-Party Computation (MPC)

MPC allows multiple participants to jointly compute a certain function while keeping their inputs confidential, without exposing any data to each other during the process. It can be understood as a collaborative computation with "built-in privacy protection."

Key applications include:

• Threshold Cryptography, enabling distributed key management
• Private Auction, ensuring that bid information is not leaked.
• Collaborative Data Analysis, modeling or decision-making without sharing raw data.

The challenge of MPC lies in the need for synchronous and honest participants. Recent innovations include: rotating committee mechanisms, verifiable secret sharing, and hybrid solutions with other PET technologies (such as MPC + ZK). Projects like Arcium are advancing practical and usable MPC networks, aiming to balance security, performance, and decentralization.

Fully Homomorphic Encryption (FHE)

FHE represents a theoretical ideal: performing computations directly on encrypted data without the need for decryption. Although the computational cost is high, FHE enables applications that were previously impossible.

Emerging Use Cases:

• Use encrypted training data for confidential machine learning
• Private Analysis of Distributed Data Sets
• Retain user-preferred encrypted proxy logic

Although FHE is still in its early stages, companies like Zama and Duality are making FHE practical.

Although still in its early stages, it holds great promise for long-term infrastructure.

Trusted Execution Environment (TEE)

TEEs like Intel SGX provide a hardware-isolated environment for privacy computing. Although they do not minimize trust in an encrypted manner, they offer practical privacy protection and high performance.

Weighing:

• Advantages: Easy integration, familiar programming model, high throughput
• Disadvantages: Manufacturer trust assumption, physical attack medium

TEE works best in hybrid systems that combine hardware privacy with cryptographic verification.

Stealth Addresses, Mixing Networks, and Metadata Concealment

• Stealth addresses separate the identity of the recipient from the transaction.
• Mix networks like Nym hide the metadata of senders/receivers.
• Relay and P2P cover anonymized interaction mode

These tools are essential for resisting censorship and maintaining anonymity, especially in messaging, asset transfers, and social use cases.

The Only Way for Multi-party Coordination

If you want to share private states without the need for centralized trust, then MPC becomes inevitable.

MPC allows multiple parties to perform joint computations on encrypted data without revealing their inputs. It is useful for the following two aspects:

• ZK-based systems: Adding expressive shared state
• FHE System: Key Custody and Threshold Decryption

Challenge:

• Who runs the MPC node?
• Performance Bottleneck
• Risk of collusion or witch hunt attacks

Nevertheless, it has made significant progress compared to single-entity DACs. Projects like Arcium, Soda Labs, and Zama are pioneering scalable MPC infrastructure and have made unique trade-offs in terms of security, performance, and governance.

Why PET is Important

Privacy is not just "optional"; it is a prerequisite for the next wave of blockchain applications:

• Game: Hidden Status and Fog of War Logic
• Governance: Anti-coercion, Private Voting
• Finance: Strategic Privacy, MEV Resistance
• Identity: Selective disclosure, prohibition of doxxing
• Artificial Intelligence: Personalized Agents, Private Model Updates

They also support compliance privacy:

• Auditing based on Zero-Knowledge Proofs
• Revocable Certificate
• Privacy within the jurisdiction

Without PET, every action on the chain is just a public performance. With PET, users regain autonomy, developers unlock new designs, and institutions gain control without centralization.

Why Privacy Is Architecturally Difficult to Achieve

The fundamental challenge lies in how to reconcile privacy with consensus. The effectiveness of blockchain comes from the ability of each node to verify every transaction. However, privacy requires hiding information from these nodes. This leads to two possible solutions:

Trusted Privacy

This method is similar to Web2 privacy, where data is hidden from the public, but trusted entities can access it. Examples include:

• Solana's token expansion, including crypto balance and authorized auditors
• Validium relies on Data Availability Committee (DAC) to obtain off-chain state.
• Private governance system with specified authority

Advantages: Easier to implement, better performance, familiar compliance model Costs: Central point of failure, regulatory capture, limited sovereignty

Trust Minimization Privacy

Here, privacy is derived from mathematics rather than institutional trust. The system uses zero-knowledge proofs (ZKP), multi-party computation (MPC), or fully homomorphic encryption (FHE) to ensure that even validators cannot access private data.

Advantages: True sovereignty, resistance to censorship, cryptographic guarantees Costs: Complex implementation, performance overhead, limited composability

The choice between these methods is not purely a technical issue; it reflects different philosophies regarding trust, control, and the purpose of blockchain systems.

Why Trustworthy Privacy Is Not Enough

Although available and reliable methods may fail when scaled:

• Single Point of Failure (a compromised DAC can leak all data)
• Poor interoperability (each application must trust the same intermediary)
• Lack of protection from regulatory agencies or subpoena threats

That said, hybrid technology is on the rise:

• Outsourced Zero-Knowledge Proofs
• Use MPC decryption for FHE computation
• An auditable system with cryptographic state commitment

Commitment (and Challenges) to Trust Minimization in Privacy To truly unlock privacy-preserving blockchains, we need to achieve programmable privacy at the protocol level, not just at the wallet or mixer layer.

Examples of projects that address this issue include:

• Penumbra, provides shielding DEX functionality
• Aztec, implementing private smart contracts through cryptographic logic
• ZK Passport, selectively disclose ID card

These systems require:

• Encrypted Execution Environment
• Privacy Protection Messaging
• Synchronization across private state machines

This often requires coordination among multiple parties, which is precisely the advantage of MPC.

Privacy Solution Assessment Framework

To address this complexity, it is necessary to evaluate the privacy system from three dimensions:

Privacy Scope

• Data Privacy: Hide transaction amounts, balances, or contract statuses
• Identity Privacy: Hides participant relationships and addresses
• Program Privacy: Encrypted contract logic and execution process
• Metadata Privacy: Hiding time, frequency, and interaction patterns

programmability

• Can developers build custom privacy protection applications?
• Are there composable privacy primitives?
• Can privacy policies be encoded and automatically executed?
• Is selective disclosure programmable and revocable?

Security Model

• What trust assumptions exist? (hardware, committee, encryption)
• Is privacy opt-in or default?
• Do these guarantees have quantum resistance?
• How is privacy compromised when it is under attack or damage?

As Vitalik Buterin said, "The strength of a chain depends on its weakest trust assumption. Strong privacy means minimizing those assumptions as much as possible."

The Privacy Design Space Beyond Payments

Currently, most research and development in privacy technology is focused on "currency", but the truly vast design space goes far beyond this, including:

• Game: Hidden state and fog of war logic
• Governance: Coercion-resistant voting system
• Identity: On-chain reputation system without the need to bind a wallet address
• Artificial Intelligence: Privacy-preserving reasoning computation for personalized intelligent agents

These systems not only require confidentiality, but also need programmable, revocable, and composable privacy mechanisms.

Leading Projects: Redefining the Privacy Landscape

Arcium

Arcium is a project focused on privacy-preserving decentralized computing, with MPC (Multi-Party Computation) as a priority at its design core. Its architecture separates key management (achieved through N/N MPC) from high-performance computing (using rotating MPC committees), achieving scalability while maintaining cryptographic security.

Key innovations include: confidential AI model training, encrypted trading strategies, and DeFi order flow privacy protection. Arcium is also researching forward privacy and quantum resistance to prepare for future infrastructure.

Aztec

Aztec is the next-generation privacy-focused Rollup that enables fully encrypted smart contract execution through its self-developed Noir programming language and zkVM (zero-knowledge virtual machine). Unlike simple mixers, Aztec not only encrypts transaction data but also encrypts the program logic itself.

Its "public-private hybrid" model allows applications to selectively share private states through cryptographic commitments, striking a balance between privacy and composability. The roadmap also includes: recursive proof technology and privacy-preserving cross-chain bridges.

Nillion

Nillion has built a brand new privacy infrastructure centered around a concept called "blind computation"—that is: computations can be performed without revealing the data content and without a consensus mechanism.

Unlike directly implementing privacy on L1 or L2, Nillion offers a decentralized computing layer to assist existing blockchains in achieving large-scale confidential computing.

Its architecture integrates various privacy-enhancing technologies (PETs), including MPC, FHE (Fully Homomorphic Encryption), TEE (Trusted Execution Environment), and ZKP, coordinated by a node network called Petnet. These nodes can process secret-shared data without communication between them, enabling fast, low-trust assumption privacy computing.

Core innovations include:

• nilDB: A distributed private key-value database for querying and storing encrypted data.
• nilVM: A virtual machine used for writing and executing blind computation logic, utilizing the custom language Nada.
• nilAI: An AI privacy infrastructure that supports training and inference on encrypted data.
• Enterprise Node Cluster: Global node operator program, partners include Vodafone, Deutsche Telekom, Alibaba Cloud, etc.

Nillion is designed for developers who need encrypted logic, secure multiparty processes, or private data analysis, applicable in fields such as healthcare, AI, identity verification, and finance. Its goal is to become the 'privacy layer of the internet', providing programmable, composable, and scalable privacy capabilities.

Penumbra

As a sovereign Cosmos chain, Penumbra introduces privacy protection at the protocol level, rather than just implementing privacy features at the application layer. Its Shielded DeFi module supports confidential transactions, staking, and governance functions through Multi-Asset Shielded Pools.

Its innovative intention-driven trading system supports encrypted order flow matching, allowing for more complex financial interaction logic while protecting market privacy.

Zama

Zama is committed to applying Fully Homomorphic Encryption (FHE) to blockchain scenarios and making it feasible in real-world environments. Through its TFHE encryption library and developer SDK, Zama has achieved the ability to perform computations on encrypted data without decryption. Zama also combines FHE with MPC for key management, creating a hybrid system that strikes a balance between security, performance, and usability, suitable for applications such as private machine learning inference and confidential data analysis.

The Path Forward: Programmable Privacy

The future is not about choosing between "transparency" and "privacy"; rather, it is about achieving programmable privacy — allowing users and applications to set precise disclosure rules:

• "Share this financial data only with verified auditors"
• "Allow access to this credential, but automatically revoke after use"
• "Disclose this transaction record only if fraud is proven mathematically."
• "Allow this AI model to learn from my data, but do not store or share the data"

To achieve this, privacy must become a "first-class citizen" in blockchain design, rather than an add-on feature tacked onto a transparent system.

Conclusion: Treat Privacy as Digital Infrastructure

Privacy is not an add-on feature for a few extreme scenarios or illegal activities. It is the foundation of digital sovereignty, and it is a prerequisite for blockchain to truly meet human needs, rather than serving "surveillance capitalism."

We are at a critical turning point: crypto tools already exist. Economic incentive mechanisms are being coordinated. The regulatory environment is evolving. What is truly needed now is a shift in consciousness: Privacy is not about "hiding", but about "choosing".

Blockchain empowers users with self-custody of assets, while Privacy-Enhancing Technologies (PETs) will grant users self-custody of information, relationships, and identity. This is precisely the difference between "holding your private key" and "holding your entire digital life."

The issue is not whether privacy will appear in the blockchain world, but whether it will come through user demand or through regulatory enforcement. Projects that are currently building privacy infrastructure are preparing for both possibilities.

Privacy is sovereignty. Privacy is choice. Privacy is the future of human-centered technology.