Abstract:
Mobile crowdsensing (MCS) systems require worker incentives to achieve good truth discovery performance. Most incentive mechanisms only compensate workers for sensing costs, neglecting privacy leakage costs.
No privacy-preserving incentive mechanism has personalized worker payments based on their privacy preferences. Paris-TD, a contract-based personalized privacy-preserving incentive mechanism for truth discovery in MCS systems, compensates workers for privacy cost while achieving accurate truth discovery.
The platform offers different contracts to workers with different privacy preferences, and each worker chooses a contract that specifies a privacy-preserving degree (PPD) and the payment she will receive if she submits perturbed data with that PPD.
We analytically design optimal contracts under full and incomplete information models that maximize truth discovery accuracy under a given budget while satisfying individual rationality and incentive compatibility. Paris-TD is tested on synthetic and real-world datasets.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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