支持模糊匹配的带标签隐私集合交集计算协议
Fuzzy Labeled Private Set Intersection Protocol
程恩泽 1张蕾 1魏立斐2
作者信息
- 1. 上海海洋大学信息学院 上海 201306
- 2. 上海海事大学信息工程学院 上海 201306
- 折叠
摘要
支持模糊匹配的带标签隐私集合交集计算协议(Fuzzy Labeled Private Set Intersection,FLPSI)是PSI协议的变体,其特点在于发送方与接收方的集合元素并不完全相等,而是存在相似性,且发送方集合中的每个元素均关联一个标签,接收方仅得到相似匹配元素的标签,而不会泄露其他信息.现有的FLPSI协议大多使用汉明距离来判断二进制向量之间的匹配程度,协议基于昂贵的公钥密码来构建,计算开销大导致协议运行缓慢.对此,提出了一种基于对称密码构造的更加高效的FLPSI协议,通过模拟范例证明了协议在半诚实模型下是安全的,参与方均无法窃取额外的隐私信息.与现有方案相比,协议将整体通信复杂度与发送方的计算复杂度由O(n2)降低为O(n).实验仿真结果表明,所提方法在平衡场景下比现有FLPSI协议快3～10倍,通信量降低89％～95％;在非平衡场景下比现有FLPSI协议快7～10倍,与类似的模糊匹配协议相比具有明显优势.此外,还设计了 FLPSI协议在隐私保护条件下人脸识别的应用,通过调整参数可以满足不同场景的要求.
Abstract
Fuzzy labeled private set intersection(FLPSI)is a variant of PSI where the elements in the sender's and receiver's sets are not the same but rather have some similarities.Each element in the sender's set is associated with a label,and the receiver on-ly receives the labels of the matched elements and without revealing other information.Most existing FLPSI protocols use Ham-ming distance to determine the degree of matching between binary vectors.These protocols are built based on expensive public key ciphers,which requiring high computation overhead and resulting in slow running time.This paper proposes an efficient FLP-SI protocol based on symmetric cryptography.It proves the security of the PSI protocol in the semi-honest model,ensuring that participants cannot obtain additional data.Compared to the existing schemes,the protocol reduces the overall communication com-plexity and the computational complexity of the sender from O(n2)to O(n).Through experimental simulation,in balanced sce-narios,the proposed protocol is 3～10x faster than the existing FLPSI protocol,and the communication is reduced by 89％to 95％.In unbalanced scenarios,the proposed protocol is 7～10x faster than the existing FLPSI protocol,and it also exhibits ob-vious advantages over similar fuzzy matching protocols.In addition,the application of FLPSI protocol in face recognition under privacy protection conditions is designed,which can meet the requirements of different scenarios by adjusting parameters.
关键词
隐私集合交集/模糊匹配/标签匹配/秘密共享/隐私计算Key words
Private set intersection/Fuzzy matching/Labeled matching/Secret sharing/Privacy preserving computation引用本文复制引用
出版年
2024
NETL
NSTL
万方数据