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Location privacy has been extensively studied in the literature with focus on specific privacy goals, privacy metrics and location privacy preserving mechanisms (LPPMs). The privacy goal defines what should be protected, which typically are single location and a sequence of locations (i.e., a trajectory). The privacy metrics define the quantitative measurement of the protection regarding a specific privacy goal. The prior art of location privacy metrics, such as geo-indistinguishability (Andrés et al., 2013) and δ-location set privacy (**ao and **ong, 2015) (**ao et al., 2017), is extending differential privacy (Dwork, 2008) to location...
References
Andrés ME, Bordenabe NE, Chatzikokolakis K, Palamidessi C (2013) Geo-indistinguishability: differential privacy for location-based systems. In: CCS, pp 901–914
Cao Y, **ao Y, **ong L, Bai L (2019a) PriSTE: from location privacy to spatiotemporal event privacy. In: 2019 IEEE 35th International Conference on Data Engineering (ICDE), pp 1606–1609
Cao Y, **ao Y, **ong L, Bai L, Yoshikawa M (2019b) PriSTE: protecting spatiotemporal event privacy in continuous location-based services. Proc VLDB Endow 12(12):1866–1869
Cao Y, **ao Y, **ong L, Bai L, Yoshikawa M (2019c) Protecting spatiotemporal event privacy in continuous location-based services pp 1–1, 1907.10814
Dwork C (2008) Differential privacy: A survey of results. In: TAMC, pp 1–19
**ao Y, **ong L (2015) Protecting locations with differential privacy under temporal correlations. In: CCS, pp 1298–1309
**ao Y, **ong L, Zhang S, Cao Y (2017) LocLok: location cloaking with differential privacy via hidden markov model. VLDB 10(12):1901–1904
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Cao, Y., **ong, L. (2021). Spatiotemporal Privacy. In: Jajodia, S., Samarati, P., Yung, M. (eds) Encyclopedia of Cryptography, Security and Privacy. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27739-9_1545-1
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DOI: https://doi.org/10.1007/978-3-642-27739-9_1545-1
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