Privacy-Preserving String Edit Distance with Moves

Nakagawa, Shunta; Sakamoto, Tokio; Takabatake, Yoshimasa; Tomohiro, I; Shin, Kilho; Sakamoto, Hiroshi

doi:10.1007/978-3-030-02224-2_18

Cited by 3 publications

(12 citation statements)

References 24 publications

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“…Finally, we compared the practical performance of our algorithm with that of sEDM [19]. Both algorithms were implemented in C++ based on the two-level HE [2] and library available from GitHub 4 , and compiled using Apple LLVM version 8.0.0 (clang-800.0.42.1) under MacOS Mojave 10.14.5.…”

Section: Resultsmentioning

confidence: 99%

“…This means that the number of characteristic substrings is relatively smaller, so the restriction of the examined n up to 10000 is reasonable. However, in reality, we cannot execute the sEDM [19] even for these repetitive strings due to the cost of relabeling shown in Table 2. By the results in Table 2 and 3, we confirm the efficiency of our algorithm for large-scale data.…”

Section: Resultsmentioning

confidence: 99%

“…Recently, Nakagawa et al proposed the first secure two-party protocol for EDM (sEDM) [19] based on HE, but, their algorithm suffers from a bottleneck during the step where the parties construct a shared labeling scheme. This motivated us to improve the previous algorithm to make it easier to use in practice.…”

Section: Related Workmentioning

confidence: 99%

“…Execution time (seconds) of approximated EDM computation for Escherichia coli (100MB). Here, n is the number of characteristic substrings used for EDM and the same rolling hash inTable 2is used for each n. L 1 -distance is computed by the sEDM[19]. A two-level HE scheme allows sufficient number of additions and a single multiplication of encrypted integers.…”

mentioning

confidence: 99%

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Faster Privacy-Preserving Computation of Edit Distance with Moves

Yoshimoto

Kataoka

Takabatake

et al. 2020

WALCOM: Algorithms and Computation

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We consider an efficient two-party protocol for securely computing the similarity of strings w.r.t. an extended edit distance measure. Here, two parties possessing strings x and y, respectively, want to jointly compute an approximate value for EDM(x, y), the minimum number of edit operations including substring moves needed to transform x into y, without revealing any private information. Recently, the first secure two-party protocol for this was proposed, based on homomorphic encryption, but this approach is not suitable for long strings due to its high communication and round complexities. In this paper, we propose an improved algorithm that significantly reduces the round complexity without sacrificing its cryptographic strength. We examine the performance of our algorithm for DNA sequences compared to previous one.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

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Faster Privacy-Preserving Computation of Edit Distance with Moves

Yoshimoto

Kataoka

Takabatake

et al. 2020

WALCOM: Algorithms and Computation

Self Cite

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“…Recently, Nakagawa et al proposed the first secure two-party protocol for EDM (sEDM) [44] based on HE. However, their algorithm suffers from a bottleneck during the step where the parties construct a shared labeling scheme.…”

Section: Related Workmentioning

confidence: 99%

Information Processing on Compressed Data

Takabatake

Tomohiro

Sakamoto

2021

Sublinear Computation Paradigm

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We survey our recent work related to information processing on compressed strings. Note that a “string” here contains any fixed-length sequence of symbols and therefore includes not only ordinary text but also a wide range of data, such as pixel sequences and time-series data. Over the past two decades, a variety of algorithms and their applications have been proposed for compressed information processing. In this survey, we mainly focus on two problems: recompression and privacy-preserving computation over compressed strings. Recompression is a framework in which algorithms transform a given compressed data into another compressed format without decompression. Recent studies have shown that a higher compression ratio can be achieved at lower cost by using an appropriate recompression algorithm such as preprocessing. Furthermore, various privacy-preserving computation models have been proposed for information retrieval, similarity computation, and pattern mining.

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