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Register a new userREAP: An Efficient Incentive Mechanism for Reconciling Aggregation Accuracy and Individual Privacy in Crowdsensing
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Added by
Zhikun Zhang
Publication date
2017
fields
Informatics Engineering
and research's language is
English
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Incentive mechanism plays a critical role in privacy-aware crowdsensing. Most previous studies on co-design of incentive mechanism and privacy preservation assume a trustworthy fusion center (FC). Very recent work has taken steps to relax the assumption on trustworthy FC and allows participatory users (PUs) to add well calibrated noise to their raw sensing data before reporting them, whereas the focus is on the equilibrium behavior of data subjects with binary data. Making a paradigm shift, this paper aim to quantify the privacy compensation for continuous data sensing while allowing FC to directly control PUs. There are two conflicting objectives in such scenario: FC desires better quality data in order to achieve higher aggregation accuracy whereas PUs prefer adding larger noise for higher privacy-preserving levels (PPLs). To achieve a good balance therein, we design an efficient incentive mechanism to REconcile FCs Aggregation accuracy and individual PUs data Privacy (REAP). Specifically, we adopt the celebrated notion of differential privacy to measure PUs PPLs and quantify their impacts on FCs aggregation accuracy. Then, appealing to Contract Theory, we design an incentive mechanism to maximize FCs aggregation accuracy under a given budget. The proposed incentive mechanism offers different contracts to PUs with different privacy preferences, by which FC can directly control PUs. It can further overcome the information asymmetry, i.e., the FC typically does not know each PUs precise privacy preference. We derive closed-form solutions for the optimal contracts in both complete information and incomplete information scenarios. Further, the results are generalized to the continuous case where PUs privacy preferences take values in a continuous domain. Extensive simulations are provided to validate the feasibility and advantages of our proposed incentive mechanism.
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In crowdsourcing markets, there are two different type jobs, i.e. homogeneous jobs and heterogeneous jobs, which need to be allocated to workers. Incentive mechanisms are essential to attract extensive user participating for achieving good service quality, especially under a given budget constraint condition. To this end, recently, Singer et al. propose a novel class of auction mechanisms for determining near-optimal prices of tasks for crowdsourcing markets constrained by the given budget. Their mechanisms are very useful to motivate extensive user to truthfully participate in crowdsourcing markets. Although they are so important, there still exist many security and privacy challenges in real-life environments. In this paper, we present a general privacy-preserving verifiable incentive mechanism for crowdsourcing markets with the budget constraint, not only to exploit how to protect the bids and assignments privacy, and the chosen winners privacy in crowdsourcing markets with homogeneous jobs and heterogeneous jobs and identity privacy from users, but also to make the verifiable payment between the platform and users for crowdsourcing applications. Results show that our general privacy-preserving verifiable incentive mechanisms achieve the same results as the generic one without privacy preservation.
Mobile crowdsensing (MCS) has been intensively explored recently due to its flexible and pervasive sensing ability. Although many incentive mechanisms have been built to attract extensive user participation, Most of these mechanisms focus only on independent task scenarios, where the sensing tasks are independent of each other. On the contrary, we focus on a periodical task scenario, where each user participates in the same type of sensing tasks periodically. In this paper, we consider the long-term user participation incentive in a general periodical MCS system from a frugality payment perspective. We explore the issue under both semi-online (the intra-period interactive process is synchronous while the inter-period interactive process is sequential and asynchronous during each period) and online user arrival models (the previous two interactive processes are sequential and asynchronous). In particular, we first propose a semi-online frugal incentive mechanism by introducing a Lyapunov method. Moreover, we also extend it to an online frugal incentive mechanism, which satisfies the constant frugality. Besides, the two mechanisms can also satisfy computational efficiency, asymptotical optimality, individual rationality and truthfulness. Through extensive simulations, we evaluate the performance and validate the theoretical properties of our online mechanisms.
Mobile Crowdsensing has shown a great potential to address large-scale problems by allocating sensing tasks to pervasive Mobile Users (MUs). The MUs will participate in a Crowdsensing platform if they can receive satisfactory reward. In this paper, in order to effectively and efficiently recruit sufficient MUs, i.e., participants, we investigate an optimal reward mechanism of the monopoly Crowdsensing Service Provider (CSP). We model the rewarding and participating as a two-stage game, and analyze the MUs participation level and the CSPs optimal reward mechanism using backward induction. At the same time, the reward is designed taking the underlying social network effects amid the mobile social network into account, for motivating the participants. Namely, one MU will obtain additional benefits from information contributed or shared by local neighbours in social networks. We derive the analytical expressions for the discriminatory reward as well as uniform reward with complete information, and approximations of reward incentive with incomplete information. Performance evaluation reveals that the network effects tremendously stimulate higher mobile participation level and greater revenue of the CSP. In addition, the discriminatory reward enables the CSP to extract greater surplus from this Crowdsensing service market.
In this paper, we study the incentive mechanism design for real-time data aggregation, which holds a large spectrum of crowdsensing applications. Despite extensive studies on static incentive mechanisms, none of these are applicable to real-time data aggregation due to their incapability of maintaining PUs long-term participation. We emphasize that, to maintain PUs long-term participation, it is of significant importance to protect their privacy as well as to provide them a desirable cumulative compensation. Thus motivated, in this paper, we propose LEPA, an efficient incentive mechanism to stimulate long-term participation in real-time data aggregation. Specifically, we allow PUs to preserve their privacy by reporting noisy data, the impact of which on the aggregation accuracy is quantified with proper privacy and accuracy measures. Then, we provide a framework that jointly optimizes the incentive schemes in different time slots to ensure desirable cumulative compensation for PUs and thereby prevent PUs from leaving the system halfway. Considering PUs strategic behaviors and combinatorial nature of the sensing tasks, we propose a computationally efficient on-line auction with close-to-optimal performance in presence of NP-hardness of winner user selection. We further show that the proposed on-line auction satisfies desirable properties of truthfulness and individual rationality. The performance of LEPA is validated by both theoretical analysis and extensive simulations.
Recently, a novel class of incentive mechanisms is proposed to attract extensive users to truthfully participate in crowd sensing applications with a given budget constraint. The class mechanisms also bring good service quality for the requesters in crowd sensing applications. Although it is so important, there still exists many verification and privacy challenges, including users bids and subtask information privacy and identification privacy, winners set privacy of the platform, and the security of the payment outcomes. In this paper, we present a privacy-preserving verifiable incentive mechanism for crowd sensing applications with the budget constraint, not only to explore how to protect the privacies of users and the platform, but also to make the verifiable payment correct between the platform and users for crowd sensing applications. Results indicate that our privacy-preserving verifiable incentive mechanism achieves the same results as the generic one without privacy preservation.
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