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Online route planning services compute routes from any given location to a desired destination address. Unlike offline implementations, they do so in a traffic-aware fashion by taking into consideration up-to-date map data and realtime traffic information. In return, users have to provide precise location information about a route's endpoints to a not necessarily trusted service provider. As suchlike leakage of personal information threatens a user's privacy and anonymity, this paper presents PrOSPR, a comprehensive approach for using current online route planning services in a privacy-preserving way, and introduces the concept of kimmune route requests to avert inference attacks based on restricted space information. Using a map-based approach for creating cloaked regions for the start and destination addresses, our solution queries the online service for routes between subsets of points from these regions. This, however, might result in the returned path deviating from the optimal route. By means of empirical evaluation on a real road network, we demonstrate the feasibility of our approach regarding quality of service and communication overhead.

Participatory sensing tries to create cost-effective, large-scale sensing systems by leveraging sensors embedded in mobile devices. One major challenge in these systems is to protect the users' privacy, since users will not contribute data if their privacy is jeopardized. Especially location data needs to be protected if it is likely to reveal information about the users' identities. A common solution is the blinding out approach that creates so-called ban zones in which location data is not published. Thereby, a user's important places, e.g., her home or workplace, can be concealed. However, ban zones of a fixed size are not able to guarantee any particular level of privacy. For instance, a ban zone that is large enough to conceal a user's home in a large city might be too small in a less populated area. For this reason, we propose an approach for dynamic map-based blinding out: The boundaries of our privacy zones, called Silent Zones, are determined in such way that at least k buildings are located within this zone. Thus, our approach adapts to the habitat density and we can guarantee k-anonymity in terms of surrounding buildings. In this paper, we present two new algorithms for creating Silent Zones and evaluate their performance. Our results show that especially in worst case scenarios, i.e., in sparsely populated areas, our approach outperforms standard ban zones and guarantees the specified privacy level.