Privacy-preserving Surveillance Methods using Homomorphic Encryption

Bowditch, William; Abramson, Will; Buchanan, William J; Pitropakis, Nikolaos; Hall, Adam J

Abstract

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.

Citation

Bowditch, W., Abramson, W., Buchanan, W. J., Pitropakis, N., & Hall, A. J. (2020, February). Privacy-preserving Surveillance Methods using Homomorphic Encryption. Presented at 6th International Conference on Information Security Systems and Privacy (ICISSP), Valletta, Malta

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 6th International Conference on Information Security Systems and Privacy (ICISSP)
• Start Date: Feb 25, 2020
• End Date: Feb 27, 2020
• Acceptance Date: Dec 3, 2019
• Publication Date: 2020
• Deposit Date: Jan 9, 2020
• Publicly Available Date: Oct 12, 2020
• Publisher: Scitepress Digital Library
• Volume: 1
• Pages: 240-248
• Book Title: ICISSP: Proceedings of the 6th International Conference on Information Systems Security and Privacy
• ISBN: 978-989-758-399-5
• DOI: https://doi.org/10.5220/0008864902400248
• Keywords: Cryptography; SEAL; Machine Learning; Homomorphic Encryption; FV (Fan and Vercauteren)
• Public URL: http://researchrepository.napier.ac.uk/Output/2461708
• Publisher URL: http://www.icissp.org/Home.aspx