A Survey on Security, Privacy, and Trust in Mobile Crowdsourcing
By Wei Feng, Zheng Yan, Hengrun Zhang, Kai Zeng, Yu Xiao, Yiwei Thomas Hou. 2018
123 citations recorded by the source corpus.
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, 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.
Subfields: Artificial Intelligence, Computer Science Applications, Sociology and Political Science
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Citations in the corpus
- Locally Differentially Private Data Collection and Analysis (2019, background): “However, privacy concerns related to user’s personal information have been serious challenges when collecting and analyzing user’s sensing data under untrusted data curators (such as in untrusted crowdsourcing systems) [55, 30, 23, 43].”