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Beyond Random Noise: Insights on Anonymization Strategies from a Latent Bandit Study
Halmstad University, School of Information Technology.ORCID iD: 0000-0002-7453-9186
Halmstad University, School of Information Technology.ORCID iD: 0000-0002-6309-2892
(Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science)ORCID iD: 0000-0003-4071-4596
Halmstad University, School of Information Technology.ORCID iD: 0000-0002-7796-5201
(English)Manuscript (preprint) (Other academic)
Abstract [en]

This paper investigates the issue of privacy in a learning scenario where users share knowledge for a recommendation task. Our study contributes to the growing body of research on privacy-preserving machine learning and underscores the need for tailored privacy techniques that address specific attack patterns rather than relying on one-size-fits-all solutions. We use the latent bandit setting to evaluate the trade-off between privacy and recommender performance by employing various aggregation strategies, such as averaging, nearest neighbor, and clustering combined with noise injection. More specifically, we simulate a linkage attack scenario leveraging publicly available auxiliary information acquired by the adversary. Our results on three open real-world datasets reveal that adding noise using the Laplace mechanism to an individual user's data record is a poor choice. It provides the highest regret for any noise level, relative to de-anonymization probability and the ADS metric. Instead, one should combine noise with appropriate aggregation strategies. For example, using averages from clusters of different sizes provides flexibility not achievable by varying the amount of noise alone. Generally, no single aggregation strategy can consistently achieve the optimum regret for a given desired level of privacy.

Keywords [en]
Latent-bandit, Privacy, Linkage-attack
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hh:diva-52138OAI: oai:DiVA.org:hh-52138DiVA, id: diva2:1815675
Available from: 2023-11-29 Created: 2023-11-29 Last updated: 2023-11-29Bibliographically approved
In thesis
1. Mobile Health Interventions through Reinforcement Learning
Open this publication in new window or tab >>Mobile Health Interventions through Reinforcement Learning
2023 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis presents work conducted in the domain of sequential decision-making in general and Bandit problems in particular, tackling challenges from a practical and theoretical perspective, framed in the contexts of mobile Health. The early stages of this work have been conducted in the context of the project ``improving Medication Adherence through Person-Centred Care and Adaptive Interventions'' (iMedA) which aims to provide personalized adaptive interventions to hypertensive patients, supporting them in managing their medication regimen. The focus lies on inadequate medication adherence (MA), a pervasive issue where patients do not take their medication as instructed by their physician. The selection of individuals for intervention through secondary database analysis on Electronic Health Records (EHRs) was a key challenge and is addressed through in-depth analysis of common adherence measures, development of prediction models for MA, and discussions on limitations of such approaches for analyzing MA. Providing personalized adaptive interventions is framed in several bandit settings and addresses the challenge of delivering relevant interventions in environments where contextual information is unreliable and full of noise. Furthermore, the need for good initial policies is explored and improved in the latent-bandits setting, utilizing prior collected data to optimal selection the best intervention at every decision point. As the final concluding work, this thesis elaborates on the need for privacy and explores different privatization techniques in the form of noise-additive strategies using a realistic recommendation scenario.         

The contributions of the thesis can be summarised as follows: (1) Highlighting the issues encountered in measuring MA through secondary database analysis and providing recommendations to address these issues, (2) Investigating machine learning models developed using EHRs for MA prediction and extraction of common refilling patterns through EHRs, (3) formal problem definition for a novel contextual bandit setting with context uncertainty commonly encountered in Mobile Health and development of an algorithm designed for such environments. (4) Algorithmic improvements, equipping the agent with information-gathering capabilities for active action selection in the latent bandit setting, and (5) exploring important privacy aspects using a realistic recommender scenario.   

Place, publisher, year, edition, pages
Halmstad: Halmstad University Press, 2023. p. 56
Series
Halmstad University Dissertations ; 102
National Category
Computer Sciences
Research subject
Health Innovation, Information driven care
Identifiers
urn:nbn:se:hh:diva-52139 (URN)978-91-89587-17-5 (ISBN)978-91-89587-16-8 (ISBN)
Public defence
2023-12-15, S1002, Kristian IV:s väg 3, Halmstad, 13:00 (English)
Opponent
Supervisors
Available from: 2023-11-29 Created: 2023-11-29 Last updated: 2024-01-03Bibliographically approved

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Galozy, AlexanderAlawadi, SadiNowaczyk, Sławomir

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