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Mobile Computing, Applications, and Services. 12th EAI International Conference, MobiCASE 2021, Virtual Event, November 13–14, 2021, Proceedings

Research Article

Privacy-Preserving Sharing of Mobile Sensor Data

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  • @INPROCEEDINGS{10.1007/978-3-030-99203-3_2,
        author={Yin Liu and Breno Dantas Cruz and Eli Tilevich},
        title={Privacy-Preserving Sharing of Mobile Sensor Data},
        proceedings={Mobile Computing, Applications, and Services. 12th EAI International Conference, MobiCASE 2021, Virtual Event, November 13--14, 2021, Proceedings},
        proceedings_a={MOBICASE},
        year={2022},
        month={3},
        keywords={},
        doi={10.1007/978-3-030-99203-3_2}
    }
    
  • Yin Liu
    Breno Dantas Cruz
    Eli Tilevich
    Year: 2022
    Privacy-Preserving Sharing of Mobile Sensor Data
    MOBICASE
    Springer
    DOI: 10.1007/978-3-030-99203-3_2
Yin Liu1,*, Breno Dantas Cruz2, Eli Tilevich3
  • 1: Faculty of Information Technology, Beijing University of Technology
  • 2: Laboratory for Software Design
  • 3: Software Innovations Lab
*Contact email: yinliu@bjut.edu.cn

Abstract

To personalize modern mobile services (e.g., advertisement, navigation, healthcare) for individual users, mobile apps continuously collect and analyze sensor data. By sharing their sensor data collections, app providers can improve the quality of mobile services. However, the data privacy of both app providers and users must be protected against data leakage attacks. To address this problem, we presentdifferentially privatized on-device sharing of sensor data, a framework through which app providers can safely collaborate with each other to personalize their mobile services. As a trusted intermediary, the framework aggregates the sensor data contributed by individual apps, accepting statistical queries against the combined datasets. A novel adaptive privacy-preserving scheme: 1) balances utility and privacy by computing and adding the required amount of noise to the query results; 2) incentivizes app providers to keep contributing data; 3) secures all data processing by integrating a Trusted Execution Environment. Our evaluation demonstrates the framework’s efficiency, utility, and safety: all queries complete in <10 ms; the data sharing collaborations satisfy participants’ dissimilar privacy/utility requirements; mobile services are effectively personalized, while preserving the data privacy of both app providers and users.

Published
2022-03-24
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-99203-3_2
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