What does “privacy” mean in blockchain networks? Why is it so difficult to achieve?

Author: Stacy Muur Translation: Shan Ouba, Golden Finance

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

Fundamental Misunderstanding

Most discussions about blockchain privacy are seriously off the mark. Privacy is often reduced to a tool for "dark web users" or directly equated with criminal activity. This narrative completely misunderstands the true meaning of privacy: privacy is not "hiding yourself", but your ability to choose when, to whom, and what information you disclose.

Think about it from another perspective: in real life, you don't announce your bank account balance to everyone you meet, you don't hand over your medical records to the cashier, and you don't share your location in real time with all businesses. You will selectively disclose information based on specific contexts, relationships, and needs. This is not antisocial behavior, but the basis of normal human interaction.

However, in Web3, we have built systems that make every transaction, every interaction, and every preference public, visible to anyone with an internet connection.

We mistake "radical transparency" for progress, when what is really needed is "radical control" - that is, letting users decide what they want to disclose and what they don't want to disclose.

Why Current Blockchain Privacy Fails

Public chains were designed to be "completely transparent" as a feature, not a defect. In the early stages, the main goal of blockchain was to prove that "decentralized currency" was feasible, so making every transaction verifiable to everyone was a necessary condition for establishing the credibility of the "trustless system".

However, as blockchain applications expanded from simple value transfer to more complex fields such as finance, identity, games, and artificial intelligence, this transparency has become a burden. Here are some real-life examples:

• Financial privacy: Your trading strategy, asset allocation, and income sources 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 binds your entertainment preferences to your wallet address;

• Strategic vulnerability: DAOs cannot discuss issues privately; companies cannot develop new products under the premise of confidentiality; individuals cannot feel at ease to try and fail, because everything will be permanently recorded and may affect their reputation.

This situation is not only uncomfortable, but also leads to a decrease in economic efficiency. Markets can work better when participants cannot get a head start on each other's strategies. Governance works better when votes cannot be bought or coerced, and innovation happens faster when experimentation does not carry permanent reputational costs.

Privacy stack layer

Before we go into detail, let’s take a look at the specific operation location of privacy functions in the blockchain technology stack:

• Application layer: user-side privacy functions, such as wallet privacy protection, encrypted messaging, etc.

• Execution layer: private smart contracts, confidential state transitions, encrypted calculations

• Consensus layer: privacy-preserving verification mechanism, encrypted block process

• Network layer: anonymous communication, metadata protection, anti-traffic analysis technology

PET: Privacy Enhancing Technologies

Privacy Enhancing Technologies (PETs) are cryptographic and system architecture foundations for building selective information disclosure and confidential computing capabilities. Rather than treating privacy as an optional "plug-in", PETs tend to embed privacy into system design.

We can divide PET into three functional categories:

1. Proof and Authentication: Prove a certain characteristic or state of data without leaking the original data

2. Private Computing: Complete computing tasks while keeping data confidential

3. Metadata and Communication Privacy: Hide the identity, time and object of the transaction

Zero-Knowledge Proof (ZKPs)

Zero-knowledge proof (ZKPs) is a cryptographic method that allows the prover to prove to the verifier that something is true without revealing the specific data content. ZKP has a wide range of applications in blockchain, including but not limited to:

• Private transactions (such as zk-rollups, Aztec, etc.)

• Identity authentication (such as ZK Email, Sismo, ZK Passport)

• Private program execution (such as Noir, zkVMs)

The main forms of ZKP include:

• zk-SNARKs: small proof size, fast verification speed, but requires trusted setup (Trusted Setup)

• zk-SNARKs: small proof size, fast verification speed, but requires trusted setup (Trusted Setup) left;">zk-STARKs: No trusted setup required, resistant to quantum computing attacks, but the proof size is large

• Recursive proof: Supports compressing complex calculations into short proofs

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

Real-world application examples:

• Achieve private transactions under auditability

• Complete identity authentication without exposing personal identity

• Use confidential input and logic to execute private computing programs

Secure Multi-Party Computation (MPC)

MPC allows multiple participants to jointly complete a function calculation on the premise that their respective inputs are confidential, and there is no need to expose any data to each other during the process. It can be understood as collaborative computing with "built-in privacy protection".

Key applications include:

• Threshold cryptography to enable distributed key management

• Private auctions to ensure bid information is not leaked

• Collaborative data analysis to model or make decisions without sharing the original data

The challenge of MPC is that it requires highly synchronized and honest participants. Recent innovations include: rotating committee mechanisms, verifiable secret sharing, and hybrid solutions with other PET technologies (such as MPC + ZK). Projects such as Arcium are advancing practical MPC networks, striving to strike a balance between security, performance and decentralization.

Fully Homomorphic Encryption (FHE)

FHE represents a theoretical ideal: performing computations directly on encrypted data without decrypting it. Although computationally expensive, FHE enables applications that were previously impossible.

Emerging Use Cases:

• Confidential machine learning with encrypted training data

• Private analytics of distributed datasets

• Encrypted proxy logic that preserves user preferences

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

While still in its early stages, it is very promising for long-term infrastructure.

Trusted Execution Environments (TEEs)

TEEs like Intel SGX provide a hardware-isolated environment for private computing. While they are not cryptographically trust-minimized, they provide practical privacy protection and high performance.

Tradeoffs:

• Pros: ease of integration, familiar programming model, high throughput

• Cons: manufacturer trust assumptions, physical attack vectors

TEEs work best in hybrid systems that combine hardware privacy with cryptographic authentication.

Stealth Addresses, Mixnets, and Metadata Hiding

• Stealth addresses separate recipient identity from transactions

• Mixnets like Nym hide sender/receiver metadata

• Relays and P2P overlays anonymize interaction patterns

These tools are critical for censorship resistance and anonymity, especially in messaging, asset transfer, and social use cases.