Towards Personalized Privacy-Preserving Incentive for Truth Discovery in Mobile Crowdsensing Systems


It is essential to have incentive mechanisms in place in mobile crowdsensing (MCS) systems in order to stimulate adequate worker participation, which is necessary for achieving good truth discovery performance. However, the majority of the existing incentive mechanisms only consider compensating the sensing cost of workers; the cost incurred by potential privacy leakage has been largely neglected. This is despite the fact that it is likely to be much higher than the cost of compensating workers. In addition, none of the existing privacy-preserving incentive mechanisms have taken into account the various privacy preferences of workers in order to personalize the payments made to those workers. In this article, we present our proposal for a contract-based, personalized, and privacy-preserving incentive mechanism for truth discovery in MCS systems called Paris-TD. This mechanism offers workers personalized payments as a compensation for the privacy cost incurred while simultaneously achieving accurate truth discovery. Each worker decides whether or not to sign a contract that specifies a privacy-preserving degree (PPD) and the corresponding payment that the worker will receive if she submits perturbed data with that PPD. The fundamental concept behind this is that the platform offers a set of different contracts to workers with different privacy preferences, and each worker chooses whether or not to sign one of these contracts. To be more specific, we design a set of optimal contracts analytically under both full and incomplete information models. These contracts are intended to maximize the truth discovery accuracy within a given budget, while simultaneously satisfying individual rationality and incentive compatibility properties. Experiments on both synthetic and real-world datasets are used to validate the feasibility and effectiveness of the Paris-TD method.

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