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Abstract-With the popularity of sensor-rich mobile devices (e.g., smart phones and wearable devices), Mobile Crowdsourcing (MCS) has emerged as an effective method for data collection and processing. Compared with traditional Wireless Sensor Networking (WSN), MCS holds many advantages such as mobility, scalability, cost-efficiency, and human intelligence. However, MCS still faces many challenges with regard to security, privacy and trust. This paper provides a survey of these challenges and discusses potential solutions. We analyze the characteristics of MCS, identify its security threats, and outline essential requirements on a secure, privacy-preserving and trustworthy MCS system. Further, we review existing solutions based on these requirements and compare their pros and cons. Finally, we point out open issues and propose some future research directions.

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Purpose: As an important measure to alleviate long-term care (LTC) costs for the disabled due to the aging of the population, long-term care insurance (LTCI) system has been paid more attention in China. In addition to the government-led public LTCI system that has been piloted in cities such as Qingdao, Chongqing and Shanghai, health insurers such as the China Life Insurance Company are also experimenting with various types of commercial LTCI in the private market. However, the commercial LTCI market is developing very slowly due to public awareness and other reasons. On the other hand, COVID-2019 has had an impact on the cognition of the importance of long-term care for the elderly due to the fact that the death cases of COVID-2019 have been mainly concentrated in the elderly population with chronic diseases such as hypertension. Therefore, the

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.