The inner product is a mathematical operation that combines two vectors to yield a single scalar value. This core algebraic operation calculates a scalar from two vectors, often conceptualized as a measure of their similarity or projection. In advanced cryptography, particularly within zero-knowledge proofs and secure multi-party computation, inner products are instrumental. They permit optimized computations over encrypted data or enable succinct verification of complex statements without revealing underlying information.
Context
The inner product’s utility in cryptographic protocols is a key topic in discussions surrounding privacy-preserving blockchain technologies and scaling solutions. Developments in zero-knowledge proof systems, such as SNARKs and STARKs, frequently rely on optimized inner product computations. News articles covering advancements in these areas often highlight the mathematical underpinnings that allow for verifiable computation while preserving data confidentiality. This concept is central to the progress of confidential transactions and verifiable decentralized applications.
A novel Multi-Client Functional Encryption scheme enables secure, privacy-preserving inner product computations over data from multiple independent sources.
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