Among many approaches for privacy-preserving biometric authentication, we focus on the approach with homomorphic encryption, which is public key encryption supporting some operations on encrypted data. In biometric authentication, the Hamming distance is often used as a metric to compare two biometric feature vectors. In this paper, we propose an efficient method to compute the Hamming distance on encrypted data using the homomorphic encryption based on ideal lattices. In our implementation of secure Hamming distance of 2048-bit binary vectors with a lattice of 4096 dimension, encryption of a vector, secure Hamming distance, and decryption respectively take about 19.89, 18.10, and 9.08 milliseconds (ms) on an Intel Xeon X3480 at 3.07 GHz. We also propose a privacy-preserving biometric authentication protocol using our method, and compare it with related protocols. Our protocol has faster performance and shorter ciphertext size than the state-of-the-art prior work using homomorphic encryption.

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Somewhat homomorphic encryption is public key encryption supporting a limited number of additions and multiplications on encrypted data. This encryption gives a powerful tool in performing meaningful computations with protecting data confidentiality, whose property is suitable mainly in cloud computing. In this paper, we focus on the scheme proposed by Brakerski and Vaikuntanathan, and present two types of packed ciphertexts in order to improve performance and reduce size of the encrypted data. One type of our packed ciphertexts is based on the message encoding technique proposed by Lauter, Naehrig and Vaikuntanathan. While their technique empowers efficient secure computation of sums and products over the integers, our second type of packed ciphertexts enables efficient secure computation of more complex functionalities such as multiple inner products and multiple Hamming distances. We apply our packing method to construct several protocols for secure biometric authentication and secure pattern matching computations. Our implementation shows that our method gives faster performance than the state‐of‐the‐art work in such applications. Copyright © 2015 John Wiley & Sons, Ltd.

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Abstract. The basic pattern matching problem is to find the locations where a pattern occurs in a text. We give several computations enabling a client to obtain matching results from a database so that the database can not learn any information about client's queried pattern. For such computations, we apply the symmetric-key variant scheme of somewhat homomorphic encryption proposed by Brakerski and Vaikuntanathan (CRYPTO 2011), which can support a limited number of both polynomial additions and multiplications on encrypted data. We also utilize the packing method introduced by Yasuda et al. (CCSW 2013) for efficiency. While they deal with only basic problems for binary vectors, we address more complex problems such as the approximate and wildcards pattern matching for non-binary vectors. To demonstrate the efficiency of our method, we implemented the encryption scheme for secure wildcards pattern matching of DNA sequences. Our implementation shows that a client can privately search real-world genomes of length 16,500 in under one second on a general-purpose PC.

We consider the torsion points of elliptc curves over certain number fields with good reduction everywhere.1