Data Freshness Quality in Privacy-Enabled Mobile Crowdsensing: Design Aspects and Challenges

It is always challenging to tackle timeliness and privacy security problems in sensing data in mobile crowdsensing (MCS) systems, and the key is in how to realize data freshness optimization in privacy-preserving scenarios. In view of the fact that existing studies on timeliness optimization of sensing data and those on privacy security enhancement are independent of each other, this paper systematically investigates data freshness optimization techniques for privacy-preserving MCS systems. Firstly, the preliminaries about MCS, privacy preservation, and sensing data quality are introduced. Then, a coupling mechanism, which intricately links the dynamics of privacy preservation and data freshness, is unveiled. This intertwining relationship introduces complexities that make the problem particularly challenging to address. Next, effective measures are summarized to realize data freshness performance enhancement in MCS. Finally, implementation, potential applications, and future directions on timely and secure MCS are discussed.