Privacy Enhancing Technologies for solving the privacy-personalization paradox : taxonomy and survey

Kaaniche, N; Laurent, M; Belguith, S

Abstract

Personal data are often collected and processed in a decentralized fashion, within
different contexts. For instance, with the emergence of distributed applications,
several providers are usually correlating their records, and providing personalized services to their clients. Collected data include geographical and indoor
positions of users, their movement patterns as well as sensor-acquired data that
may reveal users’ physical conditions, habits and interests. Consequently, this
may lead to undesired consequences such as unsolicited advertisement and even
to discrimination and stalking. To mitigate privacy threats, several techniques
emerged, referred to as Privacy Enhancing Technologies, PETs for short.
On one hand, the increasing pressure on service providers to protect users’ privacy resulted in PETs being adopted. One the other hand, service providers
have built their business model on personalized services, e.g. targeted ads and
news. The objective of the paper is then to identify which of the PETs have the
potential to satisfy both usually divergent - economical and ethical - purposes.
This paper identifies a taxonomy classifying eight categories of PETs into three
groups, and for better clarity, it considers three categories of personalized services. After defining and presenting the main features of PETs with illustrative
examples, the paper points out which PETs best fit each personalized service
category.
Then, it discusses some of the inter-disciplinary privacy challenges that may
slow down the adoption of these techniques, namely: technical, social, legal and
economic concerns. Finally, it provides recommendations and highlights several
research directions.

Citation

Kaaniche, N., Laurent, M., & Belguith, S. (2020). Privacy Enhancing Technologies for solving the privacy-personalization paradox : taxonomy and survey. Journal of Network and Computer Applications, 171, 102807. https://doi.org/10.1016/j.jnca.2020.102807

• Journal Article Type: Article
• Acceptance Date: Aug 11, 2020
• Online Publication Date: Aug 30, 2020
• Publication Date: Dec 1, 2020
• Deposit Date: Sep 2, 2020
• Publicly Available Date: Sep 2, 2020
• Journal: Journal of Network and Computer Applications
• Print ISSN: 1084-8045
• Publisher: Elsevier
• Volume: 171
• Pages: 102807
• DOI: https://doi.org/10.1016/j.jnca.2020.102807
• Publisher URL: https://doi.org/10.1016/j.jnca.2020.102807
• Related Public URLs: http://www.journals.elsevier.com/journal-of-network-and-computer-applications/