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

Abstract

Personal data are often collected and processed in a decentralized fashion, withindifferent 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 indoorpositions of users, their movement patterns as well as sensor-acquired data thatmay reveal users’ physical conditions, habits and interests. Consequently, thismay lead to undesired consequences such as unsolicited advertisement and evento discrimination and stalking. To mitigate privacy threats, several techniquesemerged, 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 providershave built their business model on personalized services, e.g. targeted ads andnews. The objective of the paper is then to identify which of the PETs have thepotential to satisfy both usually divergent - economical and ethical - purposes.This paper identifies a taxonomy classifying eight categories of PETs into threegroups, and for better clarity, it considers three categories of personalized services. After defining and presenting the main features of PETs with illustrativeexamples, the paper points out which PETs best fit each personalized servicecategory.Then, it discusses some of the inter-disciplinary privacy challenges that mayslow down the adoption of these techniques, namely: technical, social, legal andeconomic concerns. Finally, it provides recommendations and highlights severalresearch directions