Abstract
Proliferation of pervasive devices capturing sensible data streams, e.g. mobility records, raise concerns on individual privacy. Even if the data is aggregated at a central server, location data may identify a particular person. Thus, the transmitted data must be guarded against re-identification and an un-trusted server. This paper overcomes limitations of previous works and provides a privacy preserving aggregation framework for distributed data streams. Individual location data is obfuscated to the server and just aggregates of k persons can be processed. This is ensured by use of Pailler’s homomorphic encryption framework and Shamir’s secret sharing procedure. In result we obtain anonymous unification of the data streams in an un-trusted environment.
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Acknowledgments
This work is funded by the EU FP7 INSIGHT (www.insight-ict.eu) project (Intelligent Synthesis and Real-time Response using Massive Streaming of Heterogeneous Data), 318225.
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Liebig, T. (2015). Privacy Preserving Centralized Counting of Moving Objects. In: Bacao, F., Santos, M., Painho, M. (eds) AGILE 2015. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-16787-9_6
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