A survey on privacy in mobile participatory sensing applications

By Delphine Reinhardt, Andreas Reinhardt, Salil S. Kanhere, Matthias Hollick. Journal of Systems and Software, 2011

449 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Science Applications, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/42d6a734a91410f5c4eb509daa810e1bcc8bc0ff

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jihun Hamm
2. Óscar García-Morchón
3. Bin Guo
4. Mikhail Belkin
5. Zheng Yan
6. Dong Xuan
7. Klaus Wehrle
8. Xingshe Zhou

In Computer Science Applications

1. Jihun Hamm
2. Bin Guo
3. Mikhail Belkin
4. Xingshe Zhou
5. Zhiwen Yu
6. Lichao Sun

In Sociology and Political Science

1. Mikhail Belkin
2. Yanmin Zhu
3. Jihun Hamm

Citations in the corpus

• A Survey on Security, Privacy, and Trust in Mobile Crowdsourcing (2018, background): “Although there are already several surveys on the security, privacy or trust in MCS [113-120, 123].”
• Distributed Social Welfare Maximization in Urban Vehicular Participatory Sensing Systems (2018, background): “1 Mobile Participatory Sensing Existing mobile participatory sensing systems mainly use in-vehicle smartphones to collect data [11], [16], [10], [12].”
• Privacy in the Internet of Things: threats and challenges (2014, background): “Extensive privacy research has been conducted focussing mostly on location-based services [52, 53, 54], detection of privacy breaches [55, 56] and privacy aware architectures for participatory sensing [57, 58].”
• Mobile Crowd Sensing and Computing (2015, background): “Therefore, new privacy-preserving approaches need to be developed to ensure that important information cannot be gleaned from mined patterns [Matatov et al. 2010; Fung et al. 2010; Christin et al. 2011].”
• A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities (2019, background): “works analyzing the state-of-art in privacy-related concerns of participatory sensing systems [16].”
• Crowd-ML: A Privacy-Preserving Learning Framework for a Crowd of Smart Devices (2015, background): “There is a vast amount of work in crowdsensing, and we focus on the systems aspect of previous work with representative papers (we refer the reader to survey papers [7] and [1]).”