What is Private Compute?
Private compute is computation on sensitive or encrypted data while preserving privacy and confidentiality.
Why Private Compute Matters
Data is precious and needs to be protected. Homomorphic encryption and zero-knowledge proofs allow us to expand on privacy capabilities which were once not possible.
Let’s take a look at a possible use case in healthcare with HIPAA compliance: performing analysis on sensitive healthcare data while maintaining patient privacy.
The input is patient data from a healthcare provider. This is the raw, unencrypted health information of patients. The output is decrypted results that can be viewed and analyzed. These results are derived from computations on the encrypted patient data, but it’s important to note that these are not the original patient records themselves.
As we can see, private compute removes privacy barriers that inhibit data sharing, especially for sensitive information like healthcare data.
Introduction to Fully Homomorphic Encryption (FHE)
Fully Homomorphic Encryption (FHE) is a form of encryption that allows computations to be performed on encrypted data without first decrypting it. But how is that possible? Does that mean anyone can change the data?
The results of computations on encrypted data, when decrypted, are identical to the results that would have been obtained by performing the same operations on unencrypted data. We are just simply adding a “privacy veil” while the operations are performed (in the FHE environment). The key task FHE accomplishes is preventing attacks that could compromise data during processing.
Zero-Knowledge Proofs (ZKPs): Enhancing Trust Without Exposure
It is important to note that ZKPs can be generated for multiple “needs” (what to prove). For example, a ZKP can prove the correctness of the computation or the integrity of the data, etc. They must satisfy three properties: completeness, soundness, and zero-knowledge
ZKPs enable blockchain networks to verify transactions and execute smart contracts without revealing sensitive information, thereby enhancing privacy, scalability, and efficiency.
Achieving Private Compute on AO
With the use of FHE and ZKP, computations can be performed privately, with their correctness publicly verifiable on the blockchain.
FHE enables computations on encrypted data, allowing modules to process sensitive information without seeing its contents. ZKPs then will prove that certain computations were performed correctly on the encrypted data, without revealing the data itself or the specific details of the computation. This proof can be verified on the blockchain.
Developer Opportunities on AO
The combination of Fully Homomorphic Encryption (FHE) and Zero-Knowledge Proofs (ZKPs) on AO will open up a world of possibilities for developers. These technologies enable the processing of sensitive data while maintaining privacy and verifiability, creating new opportunities, especially for ML and AI applications. Some big areas for innovation include privacy-preserving LLMs, secure federated learning, and secure data marketplaces to name a few.
Real-World Use Case: PADO Labs’ Verifiable Confidential Computation (VCC) Network
PADO offers zkFHE-based decentralized computing units, providing trustless and confidential computing capabilities.
zkFHE combines Fully Homomorphic Encryption (FHE) with Zero-Knowledge Proofs (ZK). This allows computations to be performed on encrypted data while providing verifiable proof of correctness without revealing the underlying information.
The network consists of PADO Nodes that execute confidential computations, a blockchain-based contract system for task and data management, and Arweave as a decentralized storage layer (they are planning on expanding soon).
Users can securely upload encrypted data, submit computation tasks, and receive verifiably correct results, all while maintaining data privacy. This infrastructure supports various use cases requiring secure data processing, aiming to foster a verifiable data economy in the Web3 era.
