Anonymous Data Reporting Strategy with Dynamic Incentive Mechanism for Participatory Sensing. (1st June 2021) - Li, Yang; Song, Hongtao; Zhao, Yunlong; Yao, Nianmin; Wang, Nianbin
Abstract : Participatory sensing is often used in environmental or personal data monitoring, wherein a number of participants collect data using their mobile intelligent devices for earning the incentives. However, a lot of additional information is submitted along with the data, such as the participant's location, IP and incentives. This multimodal information implicitly links to the participant's identity and exposes the participant's privacy. In order to solve the issue of these multimodal information associating with participants' identities, this paper proposes a protocol to ensure anonymous data reporting while providing a dynamic incentive mechanism simultaneously. The proposed protocol first establishes a submission schedule by anonymously selecting a slot in a vector by each member where every member and system entities are oblivious of other members' slots and then uses this schedule to submit the all members' data in an encoded vector through bulk transfer and multiplayer dining cryptographers networks (DC-nets) . Hence, the link between the data and the member's identity is broken. The incentive mechanism uses blind signature to anonymously mark the price and complete the micropayments transfer. Finally, the theoretical analysis of the protocol proves the anonymity, integrity, and efficiency of this protocol. We implemented and tested the protocol on Android phones. The experiment results show that the protocol is efficient for low latency tolerable applications, which is the cases with most participatory sensing applications, and they also show the advantage of our optimization over similar anonymous data reporting protocols.