Proximity Searchable Encryption for the Iris Biometric
Proximity Searchable Encryption for the Iris Biometric
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DOI：
10.1145/3488932.3497754
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发表时间：
2022-05
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通讯作者：
Chloé Cachet;Sohaib Ahmad;Luke Demarest;Ariel Hamlin;Benjamin Fuller
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作者：
Chloé Cachet;Sohaib Ahmad;Luke Demarest;Ariel Hamlin;Benjamin Fuller
生物识别数据库收集人们的信息，并允许用户执行邻近搜索（查找查询点的有限距离内的所有记录），几乎没有密码保护。本文研究了应用于虹膜生物特征识别的邻近搜索加密技术。先前的工作提出了内积函数加密作为构建邻近生物特征数据库的技术（Kim等人，SCN 2018）。这是因为二进制汉明距离可以使用内积计算。这项工作确定并缩小了使用内积加密进行生物特征搜索的两个差距：生物特征自然使用长向量，通常具有数千位。许多内积加密方案生成一个随机矩阵，其维数与向量大小成比例，并且必须对该矩阵求逆。因此，除非我们降低向量的维数，否则在商品硬件上设置是不可行的。我们探索最先进的技术，以减少虹膜生物特征的尺寸，并表明所有已知的技术损害所得到的系统的准确性。也就是说，对于小向量大小，在搜索中返回多个不相关的生物特征。对于长度为64的向量，在返回搜索到的生物特征的概率为90%时，平均错误地返回10%的存储记录。而不是改变特征提取器，我们引入了一种新的加密技术，允许一个生成几个较小的矩阵。对于长度为1024的向量，这将运行设置的时间从23天减少到4分钟。在这个向量长度上，对于相同的$90%$被返回的被搜索的生物特征的概率，平均0.02%的存储记录被错误地返回。先验内积接近查询和所有存储记录之间的泄漏距离。我们称之为距离揭示。我们展示了一个自然的构造，从函数隐藏，秘密密钥，谓词，内积加密（沈，施，和沃茨，TCC 2009）。我们的构造只泄漏访问模式，以及返回的记录与查询的距离相同。我们把这个方案称为距离隐藏。我们实现了一个距离显示和一个距离隐藏方案，并进行了基准测试。距离揭示方案可以在4分钟内搜索一个小的（数百个）数据库，而距离隐藏方案还不实用，需要3.5小时。
Biometric databases collect people's information and allow users to perform proximity searches (finding all records within a bounded distance of the query point) with few cryptographic protections. This work studies proximity searchable encryption applied to the iris biometric. Prior work proposed inner product functional encryption as a technique to build proximity biometric databases (Kim et al., SCN 2018). This is because binary Hamming distance is computable using an inner product. This work identifies and closes two gaps to using inner product encryption for biometric search: Biometrics naturally use long vectors often with thousands of bits. Many inner product encryption schemes generate a random matrix whose dimension scales with vector size and have to invert this matrix. As a result, setup is not feasible on commodity hardware unless we reduce the dimension of the vectors. We explore state of the art techniques to reduce the dimension of the iris biometric and show that all known techniques harm the accuracy of the resulting system. That is, for small vector sizes multiple unrelated biometrics are returned in the search. For length 64 vectors, at a 90% probability of the searched biometric being returned, 10% of stored records are erroneously returned on average. Rather than changing the feature extractor, we introduce a new cryptographic technique that allows one to generate several smaller matrices. For vectors of length 1024 this reduces time to run setup from 23 days to 4 minutes. At this vector length, for the same $90%$ probability of the searched biometric being returned, .02% of stored records are erroneously returned on average. Prior inner product approaches leak distance between the query and all stored records. We refer to these as distance-revealing. We show a natural construction from function hiding, secret-key, predicate, inner product encryption (Shen, Shi, and Waters, TCC 2009). Our construction only leaks access patterns, and which returned records are the same distance from the query. We refer to this scheme as distance-hiding. We implement and benchmark one distance-revealing and one distance-hiding scheme. The distance-revealing scheme can search a small (hundreds) database in 4 minutes while the distance-hiding scheme is not yet practical, requiring 3.5 hours.