Forward Secure and Finegrained Data Sharing for Mobile Crowdsensing Secure taskdriven data sharing can improve the sensing data usage and protect data confidentiality in mobile crowdsensing (MCS). However, the existing data sharing schemes lack efficient support of forward secrecy, i.e., if the secret key of a data requester is compromised, all the historically shared data will be leaked. In this paper, we propose a forward secure and finegrained data sharing scheme in mobile crowdsensing to provide a strong security guarantee and flexible access control over the sensing data. Specifically, by incorporating puncturable encryption and attribute based encryption, a shared symmetric key for data sharing can be encrypted by an access structure over the attributes of data requesters and the introduced Bloom filter attributes. Moreover, the shared key establishment between the MCS server and data requesters can be done jointly with the both sides authentication. By utilizing the structure of the Bloom filter, the update of a private key which is used to achieve forward secrecy only needs several deletion operations and no communication with the key distributor is involved. The security proof shows our scheme is provably secure under the security model. Experiment results demonstrate the practicability of the scheme.

Incentivizing for Truth Discovery in Edgeassisted Largescale Mobile Crowdsensing The recent development of humancarried mobile devices has promoted the great development of mobile crowdsensing systems. Most existing mobile crowdsensing systems depend on the crowdsensing service of the deep cloud. With the increasing scale and complexity, there is a tendency to enhance mobile crowdsensing with the edge computing paradigm to reduce latency and computational complexity, and improve the expandability and security. In this paper, we propose an integrated solution to stimulate the strategic users to contribute more for truth discovery in the edgeassisted mobile crowdsensing. We design an incentive mechanism consisting of truth discovery stage and budget feasible reverse auction stage. In truth discovery stage, we estimate the truth for each task in both deep cloud and edge cloud. In budget feasible reverse auction stage, we design a greedy algorithm to select the winners to maximize the quality function under the budget constraint. Through extensive simulations, we demonstrate that the proposed mechanism is computationally efficient, individually rational, truthful, budget feasible and constant approximate. Moreover, the proposed mechanism shows great superiority in terms of estimation precision and expandability.