Secure Hamming distance computation for biometrics using ideal-lattice and ring-LWE homomorphic encryption
Secure Hamming distance computation for biometrics using ideal-lattice and ring-LWE homomorphic encryption
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DOI：
10.1080/19393555.2017.1293199
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发表时间：
2017-03
期刊：
影响因子：
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通讯作者：
Masaya Yasuda
中科院分区：
文献类型：
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作者：
Masaya Yasuda
摘要随着生物识别技术的广泛发展，人们对安全和隐私的关注也在迅速增加。同态加密使我们能够在不解密的情况下对加密数据进行操作，可以应用于构建隐私保护的生物特征识别系统。在这篇文章中，我们应用了两种基于理想格的同态加密方案和环-LWE(有误差学习)方案，这两种方案在分圆域的整数环上都具有同态正确性。我们比较了这两种方案在隐私保护生物特征识别中的应用。在生物特征识别中，汉明距离被用作比较两个生物特征向量以进行认证的度量。我们提出了一种有效的安全海明距离的方法。我们的方法可以将生物特征向量打包成一个密文，并且可以在打包的密文上高效地计算安全汉明距离。
ABSTRACT With widespread development of biometrics, concerns about security and privacy are rapidly increasing. Homomorphic encryption enables us to operate on encrypted data without decryption, and it can be applied to construct a privacy-preserving biometric system. In this article, we apply two homomorphic encryption schemes based on ideal-lattice and ring-LWE (Learning with Errors), which both have homomorphic correctness over the ring of integers of a cyclotomic field. We compare the two schemes in applying them to privacy-preserving biometrics. In biometrics, the Hamming distance is used as a metric to compare two biometric feature vectors for authentication. We propose an efficient method for secure Hamming distance. Our method can pack a biometric feature vector into a single ciphertext, and it enables efficient computation of secure Hamming distance over our packed ciphertexts.