IncogniSense: An anonymity-preserving reputation framework for participatory sensing applications

Reputation systems are fundamental for assessing the quality of user contributions in participatory sensing. However, naively associating reputation scores to contributions allows adversaries to establish links between multiple contributions and thus de-anonymize users. We present the IncogniSense framework as a panacea to these privacy threats. IncogniSense utilizes periodic pseudonyms generated using blind signature and relies on reputation transfer between these pseudonyms. Simulations are used to analyze various reputation cloaking schemes that address the inherent trade-off between anonymity protection and loss in reputation. Our threat analysis confirms the robustness of IncogniSense and a prototype demonstrates that associated overheads are minimal.