ZKPs, FHE, MPC: Managing Private State in Blockchains

Introduction

Blockchain technology started a revolution by removing intermediaries from transactions. But as we move towards a decentralized future, one big question remains: How do we keep our data private on a public ledger?

In this blog, we'll explore three key technologies that help manage private data on blockchains:

• Zero-Knowledge Proofs (ZKPs)
• Fully Homomorphic Encryption (FHE)
• Secure Multi-Party Computation (MPC)

We'll break down what they are, how they work, and how they help keep your data secure. Whether you're a beginner or looking to deepen your understanding, this guide is for you.

Why Privacy Matters in Blockchains

Blockchains are transparent by nature. Every transaction is recorded on a public ledger, which anyone can inspect. While this transparency brings trust, it also means your transaction history and balances are out in the open.

This lack of privacy can be a problem for:

• Individuals who don't want their financial history public.
• Businesses that need to keep transactions confidential.
• Applications that require private data handling.

Types of Private Data on Blockchains

Before diving into the technologies, let's understand the types of private data:

1. Personal Private State (PPS): Data owned by a single person or entity.
2. Shared Private State (SPS): Data that multiple people can access or modify.

Zero-Knowledge Proofs (ZKPs)

What Are ZKPs?

Zero-Knowledge Proofs allow someone to prove they know a piece of information without revealing the information itself.

Imagine: Proving to a club bouncer that you're over 18 without showing your ID.

How Do They Work?

At a high level, ZKPs involve two parties:

• Prover: The one who knows the secret.
• Verifier: The one who needs proof that the prover knows the secret.

The prover uses cryptographic techniques to convince the verifier without revealing any additional information.

Applications in Blockchains

Private Transactions: Conceal transaction details while proving they're valid.

Anonymous Credentials: Prove you have certain attributes without revealing them.

Advantages

• Strong Privacy: Data never leaves your device unencrypted.
• Security: Only the owner can decrypt and manipulate their data.

Limitations

• Computation Happens Locally: Users need capable devices.
• Limited Composability: It is hard to perform transactions involving multiple private states at once.
• Discovery Challenges: It is difficult to know if you've received new private data without scanning everything.

Fully Homomorphic Encryption (FHE)

What Is FHE?

Fully Homomorphic Encryption allows computations to be performed on encrypted data without needing to decrypt it first.

Imagine: Giving a locked box to someone who can perform calculations on the contents without opening it.

How Does It Work?

• Data is encrypted with a special encryption that supports mathematical operations.
• Computations are performed directly on the encrypted data.
• The result, still encrypted, can be decrypted by the data owner.

Applications in Blockchains

• Private Smart Contracts: Execute code over encrypted data.
• Shared Private States: Anyone can compute over encrypted data without accessing the raw data.Example: A private decentralized exchange where trades happen without revealing order details.

Advantages

• Composability: Multiple users can interact with the same private state.
• Better User Experience: Computations are done by network validators, not the user's device.
• Developer-Friendly: Can integrate with existing blockchain frameworks.

Limitations

• Trust in Validators: Validators can decrypt results, so they must be trusted not to leak data.
• Performance: Computations are much slower compared to normal operations.
• Privacy Leakage: Repeated computations might reveal patterns or information.

Secure Multi-Party Computation (MPC)

What Is MPC?

Secure Multi-Party Computation allows multiple parties to compute a function over their inputs while keeping those inputs private.

Imagine: Three friends want to know who has the highest salary without revealing their actual salaries.

How Does It Work?

• Each party splits their input into pieces and shares them securely.
• They perform computations on these pieces collaboratively.
• The final result is obtained without anyone learning the others' inputs.

Applications in Blockchains

• Dark Pool Exchanges: Allow trades without revealing order details.Example: Renegade Finance uses MPC for private order books.
• Private Voting Systems: Tally votes without revealing individual choices.
• Collaborative AI Training: Train models on private data without exposing the data.

Advantages

• Strong Privacy: Data remains private among participants.
• Flexible Computations: Can perform complex functions collaboratively.

Limitations

• Requires Cooperation: All parties need to participate.
• Potential for Censorship: Participants can refuse to cooperate, halting computations.
• Limited Scalability: More participants can make computations slower and more complex.

Comparing ZKPs, FHE, and MPC

Combining Technologies for Enhanced Privacy

In practice, these technologies are often combined to balance their strengths and weaknesses.

• ZKPs + MPC: Use ZKPs to ensure that participants in MPC are following the rules without revealing their data.
• FHE + ZKPs: Use FHE for computations and ZKPs to verify results without exposing data.
• MPC + FHE: Combine to enable computations over shared private data with enhanced security.

Real-World Examples

• Renegade Finance: Uses MPC and ZKPs for a private trading platform.
• Aztec Network: Implements ZKPs for private smart contracts.

Practical Applications

Anonymous Social Media

• Use Case: Allow users to post anonymously while proving certain attributes (like owning a specific NFT).
• Technology: ZKPs enable proof without revealing identity.

Private Voting

• Use Case: Conduct polls where votes are secret, but results are verifiable.
• Technology: FHE or MPC can be used to tally votes without exposing individual choices.

Enterprise Payments

• Use Case: Businesses can transact without revealing counterparties or amounts.
• Technology: ZKPs ensure transaction validity without exposing details.

Information-Incomplete Games

• Use Case: Games like poker where players' hands are private.
• Technology: FHE allows game logic to execute without revealing players' cards.

Conclusion

Privacy in blockchains is a complex but crucial topic. ZKPs, FHE, and MPC each offer unique solutions to managing private data on public ledgers. By understanding these technologies, we move closer to a future where privacy and decentralization go hand in hand.

Whether you're building the next big dApp or just curious about blockchain privacy, embracing these technologies will empower you to be part of the evolution.

About the Author

Hi, I’m Jaypalsinh Jadeja ! I’m a marketer and community builder with experience in Web3 and the creator economy.

I help businesses grow through content creation, SEO, and social media strategies. Let’s connect and make Web3 easier for everyone!

You can find more about me on Linktree.