New Privacy-Preserving Method for Matching Location Data

Ushida, Mebae; Yamaoka, Yuji; Itoh, Kazuya; Tsuda, Hiroshi

doi:10.1109/imis.2014.89

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…While private one-to-one matchings can reliably be done with pairwise exchanged keys, one-to-many matchings with proximity tests against an unknown number of strange users fail due to the bad scalability, especially regarding key exchange and pairwise distance calculation. Instead, spatial generalization as proposed in [11] can be applied. A MIX network [12] is a way to ensure that the network traffic and the corresponding devices are unlinkable.…”

Section: Background and Related Workmentioning

confidence: 99%

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Privacy-Preserving Collaborative Blind Macro-Calibration of Environmental Sensors in Participatory Sensing

Markert¹,

Budde²,

Schindler³

et al. 2017

EAI Endorsed Trans IoT

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The ubiquity of ever-connected smartphones has lead to new sensing paradigms that promise environmental monitoring in unprecedented temporal and spatial resolution. Everyday people may use low-cost sensors to collect environmental data. However, measurement errors increase over time, especially with low-cost air quality sensors. Therefore, regular calibration is important. On a larger scale and in participatory sensing, this needs be done in-situ. Since for this step, personal sensor data, time and location need to be exchanged, privacy implications arise. This paper presents a novel privacy-preserving multi-hop sensor calibration scheme, that combines Private Proximity Testing and an anonymizing MIX network with cross-sensor calibration based on rendezvous. Our evaluation with simulated ozone measurements and real-world taxicab mobility traces shows that our scheme provides privacy protection while maintaining competitive overall data quality in dense participatory sensing networks.

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Section: Background and Related Workmentioning

confidence: 99%

“…This complexity is not manageable in a large-scale network of mutually strange nodes. Instead, we utilize a reduction of proximity testing to equality testing via spatial generalization similar to [11]. The positions are mapped to cells via a globally deterministic function and the resulting cells are compared for equality.…”

Section: Approachmentioning

confidence: 99%