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Using Differential Privacy for the Internet of Things

Abstract : In this paper we propose a hybrid privacy-protection model for the Internet of Things (IoT) with the ultimate purpose of balancing privacy restrictions and usability in data delivery services. Our model uses traditional de-identification methods (such as k-anonymity) under low-privacy requirements, but allows for the transmission of aggregate statistical results (calculated with a privacy-preserving method such as Differential Privacy) as an alternative if the privacy requirements exceed a threshold. We show a prototype implementation for this model, and present a small step-by-step example.
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Carlos Gómez Rodríguez, Elena Barrantes S.. Using Differential Privacy for the Internet of Things. Anja Lehmann; Diane Whitehouse; Simone Fischer-Hübner; Lothar Fritsch; Charles Raab. Privacy and Identity Management. Facing up to Next Steps : 11th IFIP WG 9.2, 9.5, 9.6/11.7, 11.4, 11.6/SIG 9.2.2 International Summer School, Karlstad, Sweden, August 21-26, 2016, Revised Selected Papers, AICT-498, Springer International Publishing, pp.201-211, 2016, IFIP Advances in Information and Communication Technology, 978-3-319-55782-3. ⟨10.1007/978-3-319-55783-0_14⟩. ⟨hal-01629160⟩

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