Mobile crowdsourcing leverages mobile devices to enable effective truth discovery by recruiting workers to collect data. However, most existing incentive mechanisms overlook workers’ privacy. Although a few studies incorporate workers’ privacy, they often impose constraints on workers’ privacy demands, which may result in budget wastage. Moreover, none of the existing privacy-aware incentives account for the influence of malicious workers on truth discovery. This paper proposes Bid-TD, a personalized incentive mechanism for privacy-aware truth discovery. Bid-TD incorporates the influence of malicious workers to enhance the truth discovery accuracy and accurately captures workers’ privacy demands to provide fair compensation. Specifically, the platform publishes tasks to workers, who then submit bids reflecting their privacy demands. Taking into account each worker’s trust and privacy demands, the platform selects participants and designs the optimal contract that specifies the privacy budget and the corresponding apportionment ratio. We analyze and design an optimal contract function, which maximizes accuracy while satisfying budget feasibility and individual rationality. Experiments on both synthetic and real datasets demonstrate the effectiveness and superiority of Bid-TD.