区块链环境中的隐私保护推荐算法研究OA北大核心CSTPCD

For the problem that recommendation algorithms in the blockchain environment are diffi-cult to resist malicious attacks and have poor recommendation results.On the one hand,a fast homo-morphic encryption algorithm based on integer vector is proposed to protect the privacy protection of us-er data,and its security is guaranteed by the LWE problem.On the other hand,an efficient recommen-dation algorithm is designed based on E2LSH,which distributes the key according to the hash bucket number,so that users under the same hash bucket can perform homomorphic encryption operations and quickly calculate the similarity.On the basic system model of blockchain+IPFS,a comparison experi-ment with the latest relevant privacy-preserving recommendation algorithms is conducted using public datasets.The results show that the algorithms in this paper have an ideal recommendation effect and speed while security and privacy are guaranteed.