The Accuracy-Privacy Trade-off of Mobile Crowdsensing

@article{Alsheikh2017TheAT,
  title={The Accuracy-Privacy Trade-off of Mobile Crowdsensing},
  author={Mohammad Abu Alsheikh and Yutao Jiao and D. Niyato and Ping Wang and Derek Leong and Z. Han},
  journal={IEEE Communications Magazine},
  year={2017},
  volume={55},
  pages={132-139}
}
Mobile crowdsensing has emerged as an efficient sensing paradigm that combines the crowd intelligence and the sensing power of mobile devices, such as mobile phones and Internet of Things gadgets. This article addresses the contradicting incentives of privacy preservation by crowdsensing users, and accuracy maximization and collection of true data by service providers. We first define the individual contributions of crowdsensing users based on the accuracy in data analytics achieved by the… Expand
Enabling Data Trustworthiness and User Privacy in Mobile Crowdsensing
Achieving Incentive, Security, and Scalable Privacy Protection in Mobile Crowdsensing Services
Consent-driven data use in crowdsensing platforms: When data reuse meets privacy-preservation
A new differential privacy preserving crowdsensing scheme based on the Owen value
Data-Oriented Mobile Crowdsensing: A Comprehensive Survey
Privacy-Aware Sensing-Quality-Based Budget Feasible Incentive Mechanism for Crowdsourcing Fingerprint Collection
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 14 REFERENCES
Incentive Mechanisms for Time Window Dependent Tasks in Mobile Crowdsensing
Mobile crowdsensing: current state and future challenges
Target Tracking via Crowdsourcing: A Mechanism Design Approach
Incentive Mechanism Design for Crowdsourcing
L-diversity: privacy beyond k-anonymity
k-Anonymity: A Model for Protecting Privacy
  • L. Sweeney
  • Computer Science
  • Int. J. Uncertain. Fuzziness Knowl. Based Syst.
  • 2002
t-Closeness: Privacy Beyond k-Anonymity and l-Diversity
...
1
2
...