Abstract
The unprecedented proliferation of mobile smart devices has propelled a promising computing paradigm, Mobile Crowd Sensing (MCS), where people share surrounding insight or personal data with others. As a fast, easy, and cost-effective way to address large-scale societal problems, MCS is widely applied into many fields, e.g., environment monitoring, map construction, public safety, etc. Despite the popularity, the risk of sensitive information disclosure in MCS poses a serious threat to the participants and limits its further development in privacy-sensitive fields. Thus, the research on privacy protection in MCS becomes important and urgent. This paper targets the privacy issues of MCS and conducts a comprehensive literature research on it by providing a thorough survey. We first introduce a typical system structure of MCS, summarize its characteristics, propose essential requirements on privacy on the basis of a threat model. Then, we survey existing solutions on privacy protection and evaluate their performances by employing the proposed requirements. In essence, we classify the privacy protection schemes into four categories with regard to identity privacy, data privacy, attribute privacy, and task privacy. Besides, we review the achievements on privacy-preserving incentives in MCS from four viewpoints of incentive measures: credit incentive, auction incentive, currency incentive, and reputation incentive. Finally, we point out some open issues and propose future research directions based on the findings from our survey.
Highlights
With the ubiquity of mobile smart devices and the development of wireless communication technology, an increasing number of people are entitled to share observations or personal insight with others, which stimulates the emergence of Mobile Crowd Sensing (MCS) [22]
& We specify the unique characteristics of MCS and summarize its security model and potential attacks on privacy, based on which we propose uniform requirements that should be taken into consideration for privacy protection
The first solution is applicable to the scenario where all Data Consumer (DC) have the same privacy requirements; the second one is proposed to deal with different privacy requirements in the real world; the last one is suitable for the case where a Data Provider (DP) can cheat on both their valuations and degree requirement
Summary
With the ubiquity of mobile smart devices and the development of wireless communication technology, an increasing number of people are entitled to share observations or personal insight with others, which stimulates the emergence of Mobile Crowd Sensing (MCS) [22]. Some surveys comprehensively consider the privacy, security, and trust issues in MCS, but lack uniform evaluation criteria for privacy protection. He et al [28] only summarized the recent research development on privacy protection and data trust problems in the context of MCS. Our work differs from the existing surveys in that it holistically covers all kinds of privacy issues in MCS, and comprehensively reviews and evaluates privacy preservation schemes in MCS with uniform criteria by taking practicality into consideration. & Based on our serious survey and discussion, we find a series of open issues and propose future research directions to motivate further efforts in the field of effective and practical privacy preservation in MCS.
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