Security, Privacy, and Incentive Provision for Mobile Crowd Sensing Systems

Abstract

Recent advances in sensing, computing, and networking have paved the way for the emerging paradigm of Mobile Crowd Sensing (MCS). The openness of such systems and the richness of data MCS users are expected to contribute to them raise significant concerns for their security, privacypreservation and resilience. Prior works addressed different aspects of the problem. But in order to reap the benefits of this new sensing paradigm, we need a holistic solution. That is, a secure and accountable MCS system that preserves user privacy, and enables the provision of incentives to the participants. At the same time, we are after a MCS architecture that is resilient to abusive users and guarantees privacy protection even against multiple misbehaving and intelligent MCS entities (servers). In this work, we meet these challenges and propose a comprehensive security and privacy-preserving architecture. With a full blown implementation, on real mobile devices, and experimental evaluation we demonstrate our system's efficiency, practicality, and scalability. Last but not least, we formally assess the achieved security and privacy properties. Overall, our system offers strong security and privacy-preservation guarantees, thus, facilitating the deployment of trustworthy MCS applications.

A Survey of the Connected Vehicle Landscape—Architectures, Enabling Technologies, Applications, and Development Areas

“…These certificates and signatures may use pseudonyms to complicate deanonymization [53], [188], though computation presents a challenge [148]. Where feasible, time-limited pseudonyms, conditional anonymity, distributed resolution authority, and passive pseudonym revocation minimize the risk of adversaries extracting identifying information from a network or pushing malicious data [159], though for some applications network scale alone may provide a means of hiding within a crowd [189].…”

A Survey on Security, Privacy, and Trust in Mobile Crowdsourcing

“…With TPM, the scheme guarantees that the data cannot be tampered by malicious workers. In [110], Gisdakis et al introduced a trusted third party for the purpose of identity and key management. The adoption of pseudonym well protects the identity privacy of workers.…”

Provably Secure Anonymous-yet-Accountable Crowdsensing with Scalable Sublinear Revocation

“…AnonySense was one of the earliest solutions that utilize group signatures for crowdsensing. As pointed in [59], the way AnonySense [28] employs group signatures renders it vulnerable to Sybil attacks [60]. Because in AnonySense, it is impossible to identify signatures from the same participant, without opening the signatures of all data reports.…”

“…Therefore, it is important to efficiently identify abusive users. SPPEAR [27] and SP-PEAR with enhanced incentive provisioning [59] are focused on both anonymity and accountability. In SPPEAR [27], BUanonymity is achieved through pseudonym-based signature approach.…”