Search result of ZAMA

In the decentralized world, privacy technology is becoming essential. Zama is at the forefront with its advanced Fully Homomorphic Encryption (FHE) technology, which redefines data privacy protection. Using its "TFHE-rs" FHE library, Zama enables encrypted data processing without decryption, ensuring privacy and security. Additionally, Zama’s fhEVM applies this technology to smart contracts, enabling private smart contracts that keep data confidential even as it circulates on the blockchain. Supported by the Concrete library, which simulates and optimizes FHE operations, Zama offers a complete privacy computing solution, advancing blockchain privacy technology.

Zama uses Fully Homomorphic Encryption (FHE) to establish a privacy layer on top of current blockchains. This ensures transaction data remains encrypted throughout the entire process. The solution offers unparalleled privacy for DeFi, payments, and on-chain governance, all while preserving transparency and verifiability. Zama is setting a new standard for privacy in the crypto ecosystem.

Fully Homomorphic Encryption (FHE) represents the cutting edge of privacy protection technology. It offers exceptional privacy safeguards and can be utilized in Web3 for securing transaction privacy, protecting AI data, and enhancing privacy in co-processing units.

Ethereum's need to scale has led to the development of Layer 2 solutions, with ZK/OP rollups emerging as key players, forming a short-term OP and long-term ZK consensus, highlighting ARB, OP, zkSync, and StarkNet as major contenders. Web3 users prioritize privacy only when it provides economic value. FHE's encryption cost further burdens the already low on-chain efficiency, and large-scale adoption is feasible only when significant benefits justify the cost. For institutional clients needing public blockchains but unwilling to disclose all information, FHE's ability to display and trade ciphertext is more suitable than ZKP.

The article explains how to construct a compliant ERC20 token using fhEVM and abstracting identity through on-chain DIDs.

FHE (Fully Homomorphic Encryption) allows third parties to perform unlimited calculations and operations on encrypted data without decryption, thus achieving combinable on-chain privacy calculations. ArkStream Capital has written an article introducing the concept, application scenarios, and ecosystem of FHE, as well as the FHE-Rollup type Layer2 solution that Fhenix is building.

The application scenarios of FHE are vast, extending beyond just Web3 and blockchain. It caters to any private data within the entire internet ecosystem. This article will introduce you to the main participants and application scenarios of the FHE track.

Exploring Fully Homomorphic Encryption-based Machine Learning (FHEML), a revolutionary technology that enables computations on encrypted data, ensuring data privacy and security. Learn about the primary use cases of FHEML, including outsourced computation, encrypted inference and encrypted training insights, as well as the top frameworks and libraries supporting FHEML development.

The main argument of this post is that if the desirable end-state is to have programmable privacy infrastructure that can handle shared private state without any single point of failure, then all roads lead to MPC. We also explore the maturity of MPC and its trust assumptions, highlight alternative approaches, compare tradeoffs, and provide an industry overview.

Crypto researcher Mustafa Hourani investigates and explores some companies building products using FHE (Fully Homomorphic Encryption). He believes that FHE may become the next big technology sweeping the industry like ZKP (Zero-Knowledge Proof), and is a key catalyst for advancing data privacy and ownership.

FHE is a novel encryption technology that addresses the limitations of zero-knowledge proofs in privacy protection and scalability. It allows for sharing and protecting private states without the need for third-party trust, and enables direct computation on encrypted data, supporting various applications.

Mind is an AI restaking solution that ensures the token economy and data security of decentralized AI networks through flexible restaking and fully homomorphic encryption for consensus security. While EigenLayer uses restaking to secure different AVSs within the Ethereum ecosystem, Mind Network uses restaking to secure the consensus of various AI networks across the entire crypto ecosystem.

This article will provide an in-depth analysis of the key role of fully homomorphic encryption (FHE) and zero-knowledge proof (ZKP) in improving the privacy of blockchain applications. It will also highlight the significance of the future development potential of these technologies in the realm of blockchain data privacy.

The core of the AI technology revolution lies in ample computing power, algorithm models, and a vast amount of training data. Currently, high-performance GPU computing power is in short supply and expensive, algorithms tend to be homogenized, and there are issues regarding data compliance and privacy protection for model training data. The decentralized and distributed storage characteristics of blockchain technology can facilitate its integration with AI.

The facial NFT minting project initiated by Privasea is trendy! Users can record their faces on the IMHUMAN (I Am Human) mobile app and mint their facial data into an NFT. This combination of facial data on-chain + NFT has resulted in over 200,000 NFTs minted since its launch at the end of April, highlighting its popularity.