Research Output

Privacy-preserving Surveillance Methods using Homomorphic Encryption

  Data analysis and machine learning methods often involve the processing of cleartext data, and where this could breach the rights to privacy. Increasingly, we must use encryption to protect all states of the data: in-transit, at-rest, and in-memory. While tunnelling and symmetric key encryption are often used to protect data in-transit and at-rest, our major challenge is to protect data within memory, while still retaining its value. Ho-momorphic encryption, thus, could have a major role in protecting the rights to privacy, while providing ways to learn from captured data. Our work presents a novel use case and evaluation of the usage of homomorphic encryption and machine learning for privacy respecting state surveillance.

  • Date:

    31 December 2020

  • Publication Status:

    Published

  • Publisher

    Scitepresss

  • DOI:

    10.5220/0008864902400248

  • Library of Congress:

    QA75 Electronic computers. Computer science

  • Dewey Decimal Classification:

    005.8 Data security

  • Funders:

    Edinburgh Napier Funded

Citation

Bowditch, W., Abramson, W., Buchanan, W. J., Pitropakis, N., & Hall, A. J. (2020). Privacy-preserving Surveillance Methods using Homomorphic Encryption. https://doi.org/10.5220/0008864902400248

Authors

Keywords

Cryptography; SEAL; Machine Learning; Homomorphic Encryption; FV (Fan and Vercauteren)

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