Privacy-preserving Surveillance Methods using Homomorphic Encryption

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

2020

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. Homomorphic 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.

Paper Citation

in Harvard Style

Bowditch W., Abramson W., Buchanan W., Pitropakis N. and Hall A. (2020). Privacy-preserving Surveillance Methods using Homomorphic Encryption. In Proceedings of the 6th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP, ISBN 978-989-758-399-5, pages 240-248. DOI: 10.5220/0008864902400248

in Bibtex Style

@conference{icissp20,
author={William Bowditch and Will Abramson and William Buchanan and Nikolaos Pitropakis and Adam Hall},
title={Privacy-preserving Surveillance Methods using Homomorphic Encryption},
booktitle={Proceedings of the 6th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP,},
year={2020},
pages={240-248},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008864902400248},
isbn={978-989-758-399-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 6th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP,
TI - Privacy-preserving Surveillance Methods using Homomorphic Encryption
SN - 978-989-758-399-5
AU - Bowditch W.
AU - Abramson W.
AU - Buchanan W.
AU - Pitropakis N.
AU - Hall A.
PY - 2020
SP - 240
EP - 248
DO - 10.5220/0008864902400248