On The Privacy-Utility Tradeoff in Participatory Sensing Systems
| Autor: | Nouha Sghaier, Mohamed Ali Moussa, Yacine Ghamri-Doudane, Rim Ben Messaoud |
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| Přispěvatelé: | Laboratoire Informatique, Image et Interaction - EA 2118 (L3I), Université de La Rochelle (ULR), Laboratoire d'Informatique Gaspard-Monge (LIGM), Centre National de la Recherche Scientifique (CNRS)-Fédération de Recherche Bézout-ESIEE Paris-École des Ponts ParisTech (ENPC)-Université Paris-Est Marne-la-Vallée (UPEM), BEN MESSAOUD, Rim, La Rochelle Université (ULR) |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Information privacy
Data collection Participatory sensing Computer science Privacy software 020206 networking & telecommunications 02 engineering and technology [INFO] Computer Science [cs] Service provider Computer security computer.software_genre Information sensitivity 020204 information systems Information leakage 0202 electrical engineering electronic engineering information engineering [INFO]Computer Science [cs] computer Mobile device Private information retrieval |
| Zdroj: | Proceedings of the 15th IEEE International Symposium on Network Computing and Applications NCA 2016 NCA 2016, Oct 2016, Boston, United States. pp.1-8 NCA |
| Popis: | International audience; The ubiquity of sensors-equipped mobile devices has enabled citizens to contribute data via participatory sensing systems. This emergent paradigm comes with various applications to improve users’ quality of life. However, the data collection process may compromise the participants’ privacy when report- ing data tagged or correlated with their sensitive information. Therefore, anonymization and location cloaking techniques have been designed to provide privacy protection, yet to some cost of data utility which is a major concern for queriers. Different from past works, we assess simultaneously the two competing goals of ensuring the queriers’ required data utility and protecting the participants’ privacy. First, we introduce a trust worthy entity to the participatory sensing traditional system. Also, we propose a general privacy-preserving mechanism that runs on this entity to release a distorted version of the sensed data in order to minimize the information leakage with its associated private information. We demonstrate how to identify a near-optimal solution to the privacy-utility tradeoff by maximizing a privacy score while considering a utility metric set by data queriers (service providers). Furthermore, we tackle the challenge of data with large size alphabets by investigating quantization techniques. Finally, we evaluate the proposed model on three different real datasets while varying the prior knowledge and the obfuscation type. The obtained results demonstrate that, for different applications, a limited distortion may ensure the participants’ privacy while maintaining about 98% of the required data utility. |
| Databáze: | OpenAIRE |
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