PPFO: A Privacy Preservation-oriented Data Freshness Optimization Framework For Mobile Crowdsensing

Abstract

Mobile crowdsensing (MCS) is an effective and timely sensing data collection manner. Privacy preservation and data freshness are the two biggest concerns for the robust MCS in the modern era. Data encryption and age of information (Aol) optimization technologies can help current MCS alleviate these two issues by processing a great volume of data messages with strong security and minimal delay. In this article, a secure and timely MCS framework (PPFO: privacy preservationori-ented data freshness optimization) is put forward to achieve the privacy preservation and data freshness optimization, that is, Aol minimization on the five-layer architecture. Particularly in the link and operation layers privacy preservation is realized by an encryption approach. Game theory methodology provides a solution to Aol optimization in the perception and transmission layers. Finally, the numerical results have shown the feasibility and effectiveness of the proposed framework.