Secure pattern matching using somewhat homomorphic encryption

Yasuda, Masaya; Shimoyama, Takeshi; Kogure, Jun; Yokoyama, Keitaro; Koshiba, Takeshi

doi:10.1145/2517488.2517497

Cited by 73 publications

(52 citation statements)

References 28 publications

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“…In [41] significant speedups were gained from the use of GPU computing, but their implementation suffered from having very large memory requirements, which eventually becomes the bottleneck of their implementation. Other implementation attempts were made but they were either incomplete implementations of an HE scheme capable only of performing one multiplication operation [43], or based on other cryptographic assumptions [13], [34], [40].…”

Section: Related Workmentioning

confidence: 99%

SHIELD: Scalable Homomorphic Implementation of Encrypted Data-Classifiers

Khedr

Gulak

Vaikuntanathan

2016

IEEE Trans. Comput.

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Abstract-Homomorphic encryption (HE) systems enable computations on encrypted data, without decrypting and without knowledge of the secret key. In this work, we describe an optimized Ring Learning With Errors (RLWE) based implementation of a variant of the HE system recently proposed by Gentry, Sahai and Waters (GSW). Although this system was widely believed to be less efficient than its contemporaries, we demonstrate quite the opposite behavior for a large class of applications. We first highlight and carefully exploit the algebraic features of the system to achieve significant speedup over the state-of-the-art HE implementation, namely the IBM homomorphic encryption library (HElib). We introduce several optimizations on top of our HE implementation, and use the resulting scheme to construct a homomorphic Bayesian spam filter, secure multiple keyword search, and a homomorphic evaluator for binary decision trees. Our results show a factor of 10× improvement in performance (under the same security settings and CPU platforms) compared to IBM HElib for these applications. Our system is built to be easily portable to GPUs (unlike IBM HElib) which results in an additional speedup of up to a factor of 103.5× to offer an overall speedup of 1035×.

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Section: Related Workmentioning

confidence: 99%

SHIELD: Scalable Homomorphic Implementation of Encrypted Data-Classifiers

Khedr

Gulak

Vaikuntanathan

2016

IEEE Trans. Comput.

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“…As in [31], we apply the somewhat homomorphic encryption (SHE) scheme proposed by Brakerski and Vaikuntanathan [7] to evaluate several matching computations homomorphically. Their scheme can be used as a building block for the construction of a fully homomorphic encryption (FHE) scheme, and it can support only a limited number of both additions and multiplications but it is much more practical than FHE 1 .…”

Section: Our Contributionsmentioning

confidence: 99%

“…Their scheme can be used as a building block for the construction of a fully homomorphic encryption (FHE) scheme, and it can support only a limited number of both additions and multiplications but it is much more practical than FHE 1 . The authors in [31] also introduced a new method in the scheme to pack a vector of certain length into a single ciphertext, and gave efficient matching computations over packed ciphertexts. In the following, we summarize our contributions; -The authors in [31] used the public-key scheme of [7,Section 3.2], whereas we apply the symmetric-key scheme of [7, Section 3.1], which is more suitable for our scenario of §1.1 and gives more practical performance (see Remark 1 in §4.2).…”

Section: Our Contributionsmentioning

confidence: 99%

Privacy-Preserving Wildcards Pattern Matching Using Symmetric Somewhat Homomorphic Encryption

Yasuda

Shimoyama

Kogure

et al. 2014

Lecture Notes in Computer Science

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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.

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“…Please see our previous works [19,21] for secure Hamming distance in SHE schemes (note that the work [19] uses the SHE scheme based on ideal lattices of [11]). Furthermore, our method can be applied to efficient computation of multiple Hamming distance values for secure pattern matching (see [20]). …”

Section: Remark 2 (Privacy Enhance Technique)mentioning

confidence: 99%

Secret computation of purchase history data using somewhat homomorphic encryption

2014

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We consider secret computation of purchase history data among two companies of different type of business in order to identify purchase patterns without revealing customer information of each company. Among several privacy-preserving approaches, we focus on homomorphic encryption, which is public-key encryption supporting meaningful computations on encrypted data. In particular, we apply the somewhat homomorphic encryption scheme proposed by Brakerski and Vaikuntanathan (CRYPTO 2011), which can support a limited number of both additions and multiplications over polynomials. The main contribution is to introduce a practical packing method in the scheme to efficiently compute the set intersection of purchase history data over packed ciphertexts. Furthermore, we implemented the scheme for several parameters corresponding to various security levels, and demonstrate the efficiency of our packing method. We hope that this work would give the first practical usage of somewhat homomorphic encryption in marketing analysis.

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Secure pattern matching using somewhat homomorphic encryption

Cited by 73 publications

References 28 publications

SHIELD: Scalable Homomorphic Implementation of Encrypted Data-Classifiers

SHIELD: Scalable Homomorphic Implementation of Encrypted Data-Classifiers

Privacy-Preserving Wildcards Pattern Matching Using Symmetric Somewhat Homomorphic Encryption

Secret computation of purchase history data using somewhat homomorphic encryption

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