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Privacy-Enhanced Participatory Sensing with Collusion Resistance and Data Aggregation

Günther, F, Manulis, M and Peter, A (2014) Privacy-Enhanced Participatory Sensing with Collusion Resistance and Data Aggregation In: 13th International Conference, CANS 2014, 2014-10-22 - 2014-10-24, Heraklion, Crete, Greece.

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Participatory sensing enables new paradigms and markets for information collection based on the ubiquitous availability of smartphones, but also introduces privacy challenges for participating users and their data. In this work, we review existing security models for privacy-preserving participatory sensing and propose several improvements that are both of theoretical and practical significance. We first address an important drawback of prior work, namely the lack of consideration of collusion attacks that are highly relevant for such multi-user settings. We explain why existing security models are insufficient and why previous protocols become insecure in the presence of colluding parties. We remedy this problem by providing new security and privacy definitions that guarantee meaningful forms of collusion resistance. We propose new collusion-resistant participatory sensing protocols satisfying our definitions: a generic construction that uses anonymous identity-based encryption (IBE) and its practical instantiation based on the Boneh-Franklin IBE scheme. We then extend the functionality of participatory sensing by adding the ability to perform aggregation on the data submitted by the users, without sacrificing their privacy. We realize this through an additively-homomorphic IBE scheme which in turn is constructed by slightly modifying the Boneh-Franklin IBE scheme. From a practical point of view, the resulting scheme is suitable for calculations with small sensor readings/values such as temperature measurements, noise levels, or prices, which is sufficient for many applications of participatory sensing.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
Date : 2014
Identification Number : 10.1007/978-3-319-12280-9_21
Contributors :
Additional Information : The original publication is available at
Depositing User : Symplectic Elements
Date Deposited : 07 Nov 2014 09:41
Last Modified : 07 Nov 2014 14:33

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