Bringing order to approximate matching: Classification and attacks on similarity digest algorithms

Martín-Pérez, Miguel; Rodríguez, Ricardo J.; Breitinger, Frank

doi:10.1016/j.fsidi.2021.301120

Cited by 5 publications

(4 citation statements)

References 6 publications

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“…Distance-Preserving Hashes (DPHs) differ from classic hashes inasmuch they map input values that are close to each other in the data space into neighbours in the hash space. Distance-preserving hashes can be defined for also categorical data: a recent work [17] describes how text hashes can preserve similarity between documents. Data-dePendent Hashes (DPHs) are a family of hash functions whose hash values depend not only on the inputs, but also on other points in the data set.…”

Section: A Distance-preserving Hashesmentioning

confidence: 99%

Hash-Comb: A Hierarchical Distance-Preserving Multi-Hash Data Representation for Collaborative Analytics

2022

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Data privacy regulations like the EU GDPR allow the use of hashing techniques to anonymize data that may contain personal information. However, hashing is well-known to destroy any possibility of performing analytics. Homomorphic crypto-systems allow computing analytics over encrypted data, but cannot guarantee privacy compliance without being coupled with specific privacy-preservation provisions. In this work, we present a novel distance-preserving hashing scheme supporting both regulatory compliance and collaborative analytics. Our scheme achieves regulatory compliance by relying on standard cryptographic hashes while preserving a controllable notion of distance between data points.

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Section: A Distance-preserving Hashesmentioning

confidence: 99%

Hash-Comb: A Hierarchical Distance-Preserving Multi-Hash Data Representation for Collaborative Analytics

2022

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“…Index Terms-Similarity digest algorithm, Approximate matching, Fuzzy hashing, Similarity hashing, Bytewise, Classification scheme Tipo de contribución: Investigación ya publicada en "Bringing Order to Approximate Matching: Classification and Attacks on Similarity Digest Algorithms," Forensic Science International: Digital Investigation, 2021 [6].…”

Section: Frank Breitingermentioning

confidence: 99%

“…For the sake of space, we have deliberately omitted a more detailed description of these properties in this paper. Further details are given in [6].…”

Section: Extended Abstractmentioning

confidence: 99%