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Incentive mechanism plays a critical role in privacy-aware crowdsensing. Mostprevious studies on co-design of incentive mechanism and privacy preservationassume a trustworthy fusion center (FC). Very recent work has taken steps torelax the assumption on trustworthy FC and allows participatory users (PUs) toadd well calibrated noise to their raw sensing data before reporting them,whereas the focus is on the equilibrium behavior of data subjects with binarydata. Making a paradigm shift, this paper aim to quantify the privacycompensation for continuous data sensing while allowing FC to directly controlPUs. There are two conflicting objectives in such scenario: FC desires betterquality data in order to achieve higher aggregation accuracy whereas PUs preferadding larger noise for higher privacy-preserving levels (PPLs). To achieve agood balance therein, we design an efficient incentive mechanism to REconcileFC's Aggregation accuracy and individual PU's data Privacy (REAP).Specifically, we adopt the celebrated notion of differential privacy to measurePUs' PPLs and quantify their impacts on FC's aggregation accuracy. Then,appealing to Contract Theory, we design an incentive mechanism to maximize FC'saggregation accuracy under a given budget. The proposed incentive mechanismoffers different contracts to PUs with different privacy preferences, by whichFC can directly control PUs. It can further overcome the information asymmetry,i.e., the FC typically does not know each PU's precise privacy preference. Wederive closed-form solutions for the optimal contracts in both completeinformation and incomplete information scenarios. Further, the results aregeneralized to the continuous case where PUs' privacy preferences take valuesin a continuous domain. Extensive simulations are provided to validate thefeasibility and advantages of our proposed incentive mechanism.

REAP: An Efficient Incentive Mechanism for Reconciling Aggregation Accuracy and Individual Privacy in Crowdsensing