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Secure Similar Sequence Query on Outsourced Genomic Data

Published: 29 May 2018 Publication History

Abstract

The growing availability of genomic data is unlocking research potentials on genomic-data analysis. It is of great importance to outsource the genomic-analysis tasks onto clouds to leverage their powerful computational resources over the large-scale genomic sequences. However, the remote placement of the data raises personal-privacy concerns, and it is challenging to evaluate data-analysis functions on outsourced genomic data securely and efficiently. In this work, we study the secure similar-sequence-query (SSQ) problem over outsourced genomic data, which has not been fully investigated. To address the challenges of security and efficiency, we propose two protocols in the mixed form, which combine two-party secure secret sharing, garbled circuit, and partial homomorphic encryptions together and use them to jointly fulfill the secure SSQ function. In addition, our protocols support multi-user queries over a joint genomic data set collected from multiple data owners, making our solution scalable. We formally prove the security of protocols under the semi-honest adversary model, and theoretically analyze the performance. We use extensive experiments over real-world dataset on a commercial cloud platform to validate the efficacy of our proposed solution, and demonstrate the performance improvements compared with state-of-the-art works.

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    cover image ACM Conferences
    ASIACCS '18: Proceedings of the 2018 on Asia Conference on Computer and Communications Security
    May 2018
    866 pages
    ISBN:9781450355766
    DOI:10.1145/3196494
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    Published: 29 May 2018

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    Author Tags

    1. genomic data outsourcing
    2. mixed protocols
    3. secure similar sequence query

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    • National Science Foundation of China

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    ASIACCS '18 Paper Acceptance Rate 52 of 310 submissions, 17%;
    Overall Acceptance Rate 418 of 2,322 submissions, 18%

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    • (2024)Privacy-Enhancing and Robust Backdoor Defense for Federated Learning on Heterogeneous DataIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.332698319(693-707)Online publication date: 2024
    • (2024)PriGenX: Privacy-Preserving Query With Anonymous Access Control for Genomic DataIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2023.326629221:2(961-974)Online publication date: Mar-2024
    • (2024)Secure similar patients query with homomorphically evaluated thresholdsJournal of Information Security and Applications10.1016/j.jisa.2024.10386185(103861)Online publication date: Sep-2024
    • (2024)Efficient privacy-preserving online medical pre-diagnosis based on blockchainThe Journal of Supercomputing10.1007/s11227-024-06486-y81:1Online publication date: 2-Nov-2024
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