A Privacy-Preserving Method Based on Server-Aided Reverse Oblivious Transfer Protocol in MCS

With the popularity of the smart phones embedded in a large number of sensors, mobile crowd sensing (MCS) applications have developed rapidly. These applications often require the participants to collect the location-related data. However, this process faces the risk of the privacy leakage. The existing anonymous and cloaking methods can still analyze the user’s identity and sensing locations by obtaining enough data with spatiotemporal correlation. In addition, the existing encryption methods not only take a lot of computation cost but also need to assume a full trusted sensing platform. In order to preserve the users’ privacy, we need to separate the user’s true identity and the sensing location. We propose a privacy-preserving method based on server-aided (or cloud-assisted) reverse oblivious transfer (ROT) protocol containing a cloud server which can compute the results of the encrypted sensing data to avoid the full trust in the sensing platform and enhance the computing efficiency in MCS. It overcomes the derivation of the malicious users in the standard malicious model through constructing the coefficients of the polynomial. Finally, the analysis demonstrates that the proposed method possesses the most efficient computing time, communication overhead and storage overhead comparing with other methods and can achieve the quality-privacy tradeoff optimization.