Abstract
Participatory sensing is a process whereby mobile device users (or participants) collect environmental data on behalf of a service provider who can then build a service based upon these data. To attract submissions of such data, the service provider will often need to incentivize potential participants by offering a reward. However, for the privacy conscious, the attractiveness of such rewards may be offset by the fact that the receipt of a reward requires users to either divulge their real identity or provide a traceable pseudonym. An incentivization mechanism must therefore facilitate data submission and rewarding in a way that does not violate participant privacy. This paper presents Privacy-Aware Incentivization (PAI), a decentralized peer-to-peer exchange platform that enables the following: (i) Anonymous, unlinkable and protected data submission; (ii) Adaptive, tunable and incentive-compatible reward computation; (iii) Anonymous and untraceable reward allocation and spending. PAI makes rewards allocated to a participant untraceable and unlinkable and incorporates an adaptive and tunable incentivization mechanism which ensures that real-time rewards reflect current environmental conditions and the importance of the data being sought. The allocation of rewards to data submissions only if they are truthful (i.e., incentive compatibility) is also facilitated in a privacy-preserving manner. The approach is evaluated using proofs and experiments.
Highlights
Participatory sensing is a form of crowdsourcing that enables service providers to capture environmental data from mobile device users
The peer-to-peer distributed approach used by Privacy-Aware Incentivization (PAI), which avoids the need for third-party components, seeks to combat this potential for inference attacks by ensuring that each interaction between a participant and a service provider is stateless; i.e., each data submission made by a participant does not contain any means by which the service provider can link it to previous submissions made by the same participant
This section describes the PAI platform which refines Identity Privacy Preserving Incentivization (IPPI) [6] to take account of the service provider’s privacy requirements (As the privacy preserving requirements R2–R4 are fundamental to the platform, they are discussed before requirement R1)
Summary
Participatory sensing is a form of crowdsourcing that enables service providers to capture environmental data from mobile device users. This paper presents Privacy-Aware Incentivization (PAI), a platform that seeks to address the privacy requirements of participants in a way that does not hinder the service provider’s ability to incentivize participants and allocate rewards to a data submission only if it is truthful (known as incentive compatibility). In addition to meeting the requirements for privacy preservation and reward allocation, PAI must ensure that data submissions received by the service provider can still be evaluated to determine whether they are truthful. This must be achieved without impinging upon the participant’s privacy.
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