3 dépôts
Computational methods for executing algorithms directly on encrypted or partitioned data to maintain processor-level blindness.
Distinct from Data Encryption: Unlike general data encryption which focuses on storage or transit, this focuses on computation over encrypted data.
Explore 3 awesome GitHub repositories matching security & cryptography · Encrypted Data Processing. Refine with filters or upvote what's useful.
fhevm is a full-stack blockchain framework designed to integrate Fully Homomorphic Encryption into smart contracts. It provides a platform for developing confidential smart contracts that can process encrypted data and execute private on-chain computations without decrypting the underlying information. The framework utilizes a coprocessor system to offload resource-intensive encrypted operations to an asynchronous service, improving blockchain performance and scalability. It incorporates a secure key management service based on multi-party computation and a zero-knowledge proof verifier to en
Utilizes a coprocessor to execute algorithms directly on encrypted data to maintain processor-level blindness.
Chainlink is a decentralized oracle network that connects smart contracts to off-chain data, computation, and real-world systems. It provides a secure and reliable infrastructure for blockchain applications to access external information, execute automated workflows, and interact with other blockchains. The network is secured by a staking-based model where node operators lock LINK tokens as collateral, which can be slashed for poor performance, incentivizing honest and accurate data delivery. The platform distinguishes itself through a comprehensive set of capabilities that extend beyond basi
Computes on encrypted data within cloud servers and returns encrypted results, keeping raw data hidden from the infrastructure provider.
SecretFlow is a privacy computing framework and platform designed for secure multi-party computation, federated learning, and privacy-preserving data analysis across independent nodes. It provides a management system to coordinate secure workloads and cryptographic tasks across a distributed cluster. The project enables joint data analysis and machine learning on partitioned datasets using cryptographic protocols. It allows for the training of models and the execution of analytical queries across multiple parties without exposing raw source information to any single participant. The framewor
Provides capabilities to run analysis algorithms directly on encrypted or partitioned data to keep raw information hidden from the processor.