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• W2013997338 abstract "Geospatial data emanating from GPS-enabled pervasive devices reflects the mobility and interactions between people and places, and poses serious threats to privacy. Most of the existing location privacy works are based on the k-Anonymity privacy paradigm. In this paper, we employ a different and stronger privacy definition called Differential Privacy. We propose a novel context-aware and non context-aware differential privacy technique. Our technique couples Kalman filter and exponential mechanism to ensure differential privacy for spatio-temporal data. We demonstrate that our approach protects outliers and provides stronger privacy than state-of-the-art works." @default.
• W2013997338 created "2016-06-24" @default.
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• W2013997338 date "2013-07-01" @default.
• W2013997338 modified "2023-10-16" @default.
• W2013997338 title "A Model for Context-Aware Location Identity Preservation Using Differential Privacy" @default.
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• W2013997338 doi "https://doi.org/10.1109/trustcom.2013.45" @default.
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