Survey: Leakage and privacy at inference time

Jegorova, M., Kaul, C., Mayor, C., O'Neil, A. Q., Weir, A., Murray-Smith, R. and Tsaftaris, S. A. (2023) Survey: Leakage and privacy at inference time. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(7), pp. 9090-9108. (doi: 10.1109/tpami.2022.3229593) (PMID:37015684)

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Abstract

Leakage of data from publicly available Machine Learning (ML) models is an area of growing significance since commercial and government applications of ML can draw on multiple sources of data, potentially including users' and clients' sensitive data. We provide a comprehensive survey of contemporary advances on several fronts, covering involuntary data leakage which is natural to ML models, potential malicious leakage which is caused by privacy attacks, and currently available defence mechanisms. We focus on inference-time leakage, as the most likely scenario for publicly available models. We first discuss what leakage is in the context of different data, tasks, and model architectures. We then propose a taxonomy across involuntary and malicious leakage, followed by description of currently available defences, assessment metrics, and applications. We conclude with outstanding challenges and open questions, outlining some promising directions for future research.

Item Type:Articles
Additional Information:This work is supported by iCAIRD, funded by Innovate UK, UK Research and Innovation (UKRI)[104690]. S.A. Tsaftaris acknowledges support by Canon Medical / Royal Academy of Engineering Research Chair, Grant RCSRF1819\8\25.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Murray-Smith, Professor Roderick and Kaul, Dr Chaitanya
Authors: Jegorova, M., Kaul, C., Mayor, C., O'Neil, A. Q., Weir, A., Murray-Smith, R., and Tsaftaris, S. A.
College/School:College of Science and Engineering > School of Computing Science
Journal Name:IEEE Transactions on Pattern Analysis and Machine Intelligence
Publisher:IEEE
ISSN:0162-8828
ISSN (Online):1939-3539
Published Online:15 December 2022
Copyright Holders:Copyright © 2023 IEEE
First Published:First published in IEEE Transactions on Pattern Analysis and Machine Intelligence 45(7):9090-9108
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
304546I-CAIRD: Industrial Centre for AI Research in Digital DiagnosticsKeith MuirInnovate UK (INNOVATE)104690Stroke & Brain Imaging