论文标题
隐私验证团体会员资格验证：稀疏还是密集？
Group Membership Verification with Privacy: Sparse or Dense?
论文作者
论文摘要
小组成员验证检查生物特征性状是否对应于组的一个成员，而不揭示该成员的身份。最近的贡献通过联合使用两种机制为小组成员协议提供了隐私：将模板量化为离散嵌入，并将几个模板汇总为一个组表示。但是，该方案有一个缺点：代表组的数据结构的大小有限，当汇总许多模板时无法识别嘈杂的查询。此外，嵌入的稀疏性似乎在性能验证中起着至关重要的作用。本文提出了一个用于小组成员验证的数学模型，允许揭示稀疏对安全性，紧凑性和验证性能的影响。该模型将缝隙桥接到Bloom Filter稳健的噪音查询。它表明，除非查询几乎没有噪音，否则密集的解决方案更具竞争力。
Group membership verification checks if a biometric trait corresponds to one member of a group without revealing the identity of that member. Recent contributions provide privacy for group membership protocols through the joint use of two mechanisms: quantizing templates into discrete embeddings and aggregating several templates into one group representation. However, this scheme has one drawback: the data structure representing the group has a limited size and cannot recognize noisy queries when many templates are aggregated. Moreover, the sparsity of the embeddings seemingly plays a crucial role on the performance verification. This paper proposes a mathematical model for group membership verification allowing to reveal the impact of sparsity on both security, compactness, and verification performances. This model bridges the gap towards a Bloom filter robust to noisy queries. It shows that a dense solution is more competitive unless the queries are almost noiseless.
