Multi random projection inner product encryption, applications to proximity searchable encryption for the iris biometric
Multi random projection inner product encryption, applications to proximity searchable encryption for the iris biometric
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DOI：
10.1016/j.ic.2023.105059
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发表时间：
2023-08
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通讯作者：
Chloé Cachet;Sohaib Ahmad;Luke Demarest;S. Riback;Ariel Hamlin;Benjamin Fuller
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作者：
Chloé Cachet;Sohaib Ahmad;Luke Demarest;S. Riback;Ariel Hamlin;Benjamin Fuller
生物识别数据库收集人们的信息并执行邻近搜索（在查询的有限距离内查找记录），几乎没有密码保护。这项工作研究了应用于虹膜生物特征的邻近可搜索加密。先前的工作提出了从内积函数加密构建邻近搜索（Kim等人，SCN 2018）。这项工作确定并弥补了这种方法中的两个差距：1.生物特征识别使用长向量，通常具有数千位。许多内积加密方案必须求逆其维数与此大小成比例的矩阵。安装程序在商用硬件上是不可行的。我们引入了一种技术，提高了设置效率，而不损害准确性。2.以前的方法泄漏查询和所有存储的记录之间的距离。我们提出了一种从函数隐藏、谓词、内积加密（Shen等人，TCC 2009），避免了这种泄漏。最后，我们表明，我们的计划可以实例化使用对称配对组，这提高了搜索效率。
Biometric databases collect people's information and perform proximity search (finding records within bounded distance of the query) with few cryptographic protections. This work studies proximity searchable encryption applied to the iris biometric.Prior work proposed to build proximity search from inner product functional encryption (Kim et al., SCN 2018). This work identifies and closes two gaps in this approach:1.Biometrics use long vectors, often with thousands of bits. Many inner product encryption schemes have to invert a matrix whose dimension scales with this size. Setup is then not feasible on commodity hardware. We introduce a technique that improves setup efficiency without harming accuracy.2.Prior approaches leak distance between queries and all stored records. We propose a construction from function hiding, predicate, inner product encryption (Shen et al., TCC 2009) that avoids this leakage.Finally, we show that our scheme can be instantiated using symmetric pairing groups, which improves search efficiency.