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

Abdelrahman Almahmoud, Ernesto Damiani, Hadi Otrok

Research output: Contribution to journalArticlepeer-review

Abstract

Data privacy regulations like the EU GDPR allow the use of hashing techniques to anonymize data that may contain personal information. However, cryptographic 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.

Original languageBritish English
Pages (from-to)34393-34403
Number of pages11
JournalIEEE Access
Volume10
DOIs
StatePublished - 2022

Keywords

  • big data
  • Data privacy
  • distance-preserving hashing
  • hashing
  • homomorphic encryption
  • quantization kit

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