Privacy protection in mobile crowd sensing: a survey

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.

A Serious Gaming Approach for Crowdsensing in Urban Water Infrastructure with Blockchain Support

“…MCS requires an incentive scheme to be effective, while the amount and accuracy of sensing data should be based on the active participation of citizens. Some of the main challenges in robust MCS systems are given by the trust, reliability and security aspects (e.g., privacy protection, malicious behavior, security threats) of crowd-sensed data from anonymous sources [13]. Solutions include reputation systems (trust, reliability) and incentives (increasing participation, compensating for privacy concerns).…”

On the Challenge of Service Recommendation to Mobile Users in Smart Cities: Context and Architecture

“…Designing a security mechanism in a system that uses the proposed context model is extremely important. In literature, many works have carried out on mobile data protection and privacy handling mechanisms ((Beierle et al, 2018), (Wang et al, 2020)), anonymization (Wang et al, 2020) seems to be a promising method because it ensures identity privacy and data protection. Encryption (Wang et al, 2020) is also a promising method to achieve data confidentiality, but it can involve a high computation complexity which is not recommended for mobile systems because of energy consumption.…”

“…In literature, many works have carried out on mobile data protection and privacy handling mechanisms ((Beierle et al, 2018), (Wang et al, 2020)), anonymization (Wang et al, 2020) seems to be a promising method because it ensures identity privacy and data protection. Encryption (Wang et al, 2020) is also a promising method to achieve data confidentiality, but it can involve a high computation complexity which is not recommended for mobile systems because of energy consumption. Also, Eisenman et al (2010) propose a sensing profiling method to ensure data security where data sensing rules are established such as where, when and which data is collected and the conditions to trigger data capture.…”

“…Also, during the collection and processing of mobility data, security mechanisms will be applied in order to protect data and identities. Anonymization (Wang et al, 2020) can be used when the data is sent to the backend in order to make user data difficult to distinguish. Encryption (Wang et al, 2020) can also be applied in the frontend to protect data confidentiality.…”

“…Anonymization (Wang et al, 2020) can be used when the data is sent to the backend in order to make user data difficult to distinguish. Encryption (Wang et al, 2020) can also be applied in the frontend to protect data confidentiality. Also, the sensing manager can adopt a sensing profile (Eisenman et al, 2010) to minimize communication with the sensors and therefore minimize the risk of attack.…”