The Utility and Privacy Effects of a Click - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year :

The Utility and Privacy Effects of a Click


Recommenders are becoming one of the main ways to navigate the Internet. They recommend appropriate items to users based on their clicks, i.e., likes, ratings, purchases, etc. These clicks are key to providing relevant recommendations and, in this sense, have a significant utility. Since clicks reflect the preferences of users, they also raise privacy concerns. At first glance, there seems to be an inherent trade-off between the utility and privacy effects of a click. Nevertheless, a closer look reveals that the situation is more subtle: some clicks do improve utility without compromising privacy, whereas others decrease utility while hampering privacy. In this paper, for the first time, we propose a way to quantify the exact utility and privacy effects of each user click. More specically, we show how to compute the privacy effect (disclosure risk) of a click using an information-theoretic approach, as well as its utility, using a commonality-based approach. We determine precisely when utility and privacy are antagonist and when they are not. To illustrate our metrics, we apply them to recommendation traces from Movielens and Jester datasets. We show, for instance, that, considering the Movielens dataset, 5.94% of the clicks improve the recommender utility without loss of privacy, whereas 16.43% of the clicks induce a high privacy risk without any utility gain. An appealing application of our metrics is what we call a click-advisor, a visual user-aware clicking platform that helps users decide whether it is actually worth clicking on an item or not (after evaluating its potential utility and privacy effects using our techniques). Using a game-theoretic approach, we evaluate several user clicking strategies. We highlight in particular what we define as a smart strategy, leading to a Nash equilibrium, where every user reaches the maximum possible privacy while preserving the average overall recommender utility for all users (with respect to the case where user clicks are based solely on their genuine preferences, i.e., without consulting the click-advisor).

Dates and versions

hal-01660733 , version 1 (11-12-2017)



Rachid Guerraoui, Anne-Marie Kermarrec, Mahsa Taziki. The Utility and Privacy Effects of a Click. SIGIR 2017 - Thr 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, Aug 2017, Tokyo, Japan. pp.665-674, ⟨10.1145/3077136.3080783⟩. ⟨hal-01660733⟩
346 View
0 Download



Gmail Facebook Twitter LinkedIn More