Privacy Preserving Centralized Counting of Moving Objects

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.