On the efficiency of privacy-preserving path hiding for mobile sensing applications

Current mobile sensing applications typically annotate the collected sensor readings with spatiotemporal information before reporting them to a central server. Such information can however endanger the users´ privacy, as it reveals insights about their daily routines. Users must therefore trust the application administrators not to misuse the reported information. To diminish user dependence on administrators trustworthiness, we propose a privacy-preserving collaborative scheme, in which users exchange the collected sensor readings at opportunistic encounters. We model malicious administrators attempting to identify exchanged sensor readings based on spatial disparity by applying four state-of-the-art outlier detection algorithms. We thoroughly investigate the influence of different exchange patterns and the parameters of the algorithms on their performance based on a real-world dataset. The results for location traces of 20 users gathered during 14 days show that our algorithm achieves a high level of privacy protection.