一种全同态加密的安全内积计算方案

A Secure Computation Scheme of Inner Product Based on Fully Homomorphic Encryption

在云计算环境下密文top-k检索的众多方法中,该文聚焦于同态加密方法,该公钥加密方法具有不解密就能对密文进行操作的优点。在密文top-k查询中,内积相似性是度量索引向量和查询向量的相似性的最常用的一个指标。该文提出一个安全计算两向量内积相似性的方案,该方案使用基于环上错误学习问题的批处理和打包的同态加密来保护隐私。与其他方法相比,该方案具有通信代价低和计算代价低的优点。

Among many approaches to solve the problem of top-k retrieval over encrypted cloud data, we focus on an approach with homomorphic encryption, which is public key encryption supporting some operations on encrypted data. In top-k retrieval of encrypted data, the inner product is often used as a metric to compute the similarity between the file feature vector and the query vector. In this paper, we propose an efficient scheme to compute the inner product on encrypted data using the homomorphic encryption based on the learning with errors over ring （RLWE） problem, in which batch and packing techniques are adopted to achieve lower computation and communication cost.