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Colmade: Collaborative Masking in Auditable Decryption for BFV-based Homomorphic Encryption

Published: 23 June 2022 Publication History

Abstract

This paper proposes a novel collaborative decryption protocol for the Brakerski-Fan-Vercauteren (BFV) homomorphic encryption scheme in a multiparty distributed setting, and puts it to use in designing a leakage-resilient biometric identification solution. Allowing the computation of standard homomorphic operations over encrypted data, our protocol reveals only one least significant bit (LSB) of a scalar/vectorized result resorting to a pool of N parties. By employing additively shared masking, our solution preserves the privacy of all the remaining bits in the result as long as one party remains honest. We formalize the protocol, prove it secure in several adversarial models, implement it on top of the open-source library Lattigo and showcase its applicability as part of a biometric access control scenario.

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  • (2023)Privacy-Preserving Convolutional Bi-LSTM Network for Robust Analysis of Encrypted Time-Series Medical ImagesAI10.3390/ai40300374:3(706-720)Online publication date: 28-Aug-2023

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      cover image ACM Conferences
      IH&MMSec '22: Proceedings of the 2022 ACM Workshop on Information Hiding and Multimedia Security
      June 2022
      177 pages
      ISBN:9781450393553
      DOI:10.1145/3531536
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      Published: 23 June 2022

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      Author Tags

      1. biometric identification
      2. decryption
      3. masking
      4. muptiparty homomorphic encryption
      5. privacy preserving technologies
      6. secure computation leakage

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      • (2023)Privacy-Preserving Convolutional Bi-LSTM Network for Robust Analysis of Encrypted Time-Series Medical ImagesAI10.3390/ai40300374:3(706-720)Online publication date: 28-Aug-2023

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