Lightweight Privacy-Preserving Averaging for the Internet of Things

Abstract : The number of connected devices is growing continuously , and so is their presence into our everyday lives. From GPS-enabled fitness trackers, to smart fridges that tell us what we need to buy at the grocery store, connected devices—things—have the potential to collect and make available significant amounts of information. On the one hand, this information may provide useful services to users, and constitute a statistical gold mine. On the other, its availability poses serious privacy threats for users. In this paper we propose a novel protocol that makes it possible to aggregate personal information collected by smart devices in the form of an average, while preventing attackers from learning the details of the non-aggregated data.
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M4IOT 2016 - 3rd Workshop on Middleware for Context-Aware Applications in the IoT, Dec 2016, Trento, Italy. ACM, pp.19 - 22, 2016, 〈10.1145/3008631.3008635〉
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Dernière modification le : mercredi 29 novembre 2017 - 15:42:33
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Tristan Allard, Davide Frey, George Giakkoupis, Julien Lepiller. Lightweight Privacy-Preserving Averaging for the Internet of Things. M4IOT 2016 - 3rd Workshop on Middleware for Context-Aware Applications in the IoT, Dec 2016, Trento, Italy. ACM, pp.19 - 22, 2016, 〈10.1145/3008631.3008635〉. 〈hal-01421986〉

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