Article
Version 1
Preserved in Portico This version is not peer-reviewed
On the Anonymity Risk of Time-Varying User Profiles
Version 1
: Received: 12 April 2017 / Approved: 13 April 2017 / Online: 13 April 2017 (04:46:21 CEST)
A peer-reviewed article of this Preprint also exists.
Puglisi, S.; Rebollo-Monedero, D.; Forné, J. On the Anonymity Risk of Time-Varying User Profiles. Entropy 2017, 19, 190. Puglisi, S.; Rebollo-Monedero, D.; Forné, J. On the Anonymity Risk of Time-Varying User Profiles. Entropy 2017, 19, 190.
Abstract
Websites and applications use personalisation services to profile their users, collect their patterns and activities and eventually use this data to provide tailored suggestions. User preferences and social interactions are therefore aggregated and analysed. Every time a user publishes a new post or creates a link with another entity, either another user, or some on-line resource, new information is added to the user profile. Exposing private data does not only reveal information about single users' preferences, increasing their privacy risk, but can expose more about their network that single actors intended. This mechanism is self-evident on \emph{social networks} where users receive suggestions based on their friends' activity. We propose an information theoretic approach to measure the differential update of the anonymity risk for time-varying user profiles. This expresses how privacy is affected when new content is posted and how much third party services get to know about the users when a new activity is shared. We use real Facebook data to show how our model can be applied on a real world scenario.
Keywords
privacy; anonymity risk; dynamic user profile, online footprints
Subject
Computer Science and Mathematics, Information Systems
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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