Privacy-preserving crowdsourced spectrum sensing

Jin, Xiaocong; Zhang, Yanchao

doi:10.1109/infocom.2016.7524588

Cited by 75 publications

(17 citation statements)

References 26 publications

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“…The work in [45], [46] has studied the privacy preserving data publishing and aggregation problems in mobile crowdsourcing, but without considering the incentivization problem. The excellent studies in [35], [47] have proposed some novel approaches to protect the location privacy of the users, using the tools of differential privacy or k-anonymity. Two closest studies to ours are [8] and [18], where some ingenious auction mechanisms are proposed to protect the bidding privacy of the users in mobile crowdsourcing.…”

Section: Related Workmentioning

confidence: 99%

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Differentially Private Mechanisms for Budget Limited Mobile Crowdsourcing

Han

Liu

Tang

et al. 2019

IEEE Trans. on Mobile Comput.

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Recently, Mobile Crowdsourcing (MC) has aroused great interest on the part of both academic and industrial circles. One of the key problems in MC is designing the proper mechanisms to incentivize user participation, as users are typically selfinterested and must consume a substantial amount of MC resources/costs. Although considerable research has been devoted to this problem, the majority of studies have neglected the privacy issue in mechanism design. In this study, we consider the scenario where a mobile crowdsourcing platform aims to maximize the crowdsourcing revenue under a budget constraint, and users are interested in maximizing their utility while keeping their cost private. We design differentially-private mechanisms for such a scenario under an offline setting where users bid their costs simultaneously and under an online setting where user bids are revealed one by one. We show that our mechanisms simultaneously achieve provable performance bounds with respect to several measures, including revenue, differential privacy, truthfulness and individual rationality. Finally, we also conduct extensive numerical experiments to demonstrate the effectiveness of our approach.✦ 1. This is based on the rationale of "individual rationality" or "voluntary participation" [11], i.e., no user who truthfully participates in the crowdsourcing campaign should be paid less than her/his private cost.Huan Liu received his B.E. degree in Computer Science from Jilin University, China, in 2015. He is currently a master's student at the School of Computer Science and Technology, USTC. His research interests include mobile crowdsourcing and privacy-preserving data management.

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Section: Related Workmentioning

confidence: 99%

“…Fortunately, in such cases, we can often get the distribution knowledge and hence the expected value of the number of participants. For example, the historical mobility traces of the users could be used to estimate the number of users appeared in the Points of Interests (PoI) of mobile crowdsourcing applications [9], [35].…”

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confidence: 99%

Differentially Private Mechanisms for Budget Limited Mobile Crowdsourcing

Han

Liu

Tang

et al. 2019

IEEE Trans. on Mobile Comput.

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“…It originally comes from the database discipline and has been applied in many other related areas [4], [36], [37]. In what follows, we first introduce the definition of -differential privacy and its properties.…”

Section: Preliminaries On Differential Privacymentioning

confidence: 99%

PriStream: Privacy-preserving distributed stream monitoring of thresholded PERCENTILE statistics

Sun

Zhang

et al. 2016

IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications

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Distributed stream monitoring has numerous potential applications in future smart cities. Communication efficiency and data privacy are two main challenges for distributed stream monitoring services. In this paper, we propose PriStream, the first communication-efficient and privacy-preserving distributed stream monitoring system for thresholded PERCENTILE aggregates. PriStream allows the monitoring service provider to evaluate an arbitrary function over a desired percentile of distributed data reports and monitor when the output exceeds a predetermined system threshold. Detailed theoretical analysis and evaluations show that PriStream has high accuracy and communication efficiency, and differential privacy guarantees under a strong adversary model.

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“…Recently, the privacy protection in MCS has attracted high attention in numerous researchers [18]- [21]. Wang et al [18] utilize the k-anonymity to reduce the risk of location-privacy disclosure for crowd sensing users.…”

Section: Introductionmentioning

confidence: 99%

“…Zhang et al [20] design an incentive mechanism to maximize the fusion center's aggregation accuracy by quantizing crowd sensing users' privacy preserving levels and characterizing their impacts on the aggregation accuracy. Paper [21] focuses on the location privacy in the crowdsourced spectrum sensing scenario and presents a novel framework, consisting of two different schemes under distinct design objectives and assumptions, for a service provider to select participants in a differentially privacy-preserving manner. Above related works focus on protecting a participant's privacy only from either other crowd sensing users or the service requestor, whereas an integrated and more reliable privacy preservation mechanism considering potential attacks from both mobile users and the requestor should also be studied.…”

Section: Introductionmentioning

confidence: 99%

Privacy-Aware Crowdsourced Spectrum Sensing and Multi-User Sharing Mechanism in Dynamic Spectrum Access Networks

Zhu

Wang

2019

IEEE Access

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Dynamic spectrum access (DSA) emerging as an effective way of improving the utilization of the scarce spectrum has attracted great attention in the communication field. A key challenge in DSA is to perform an efficient spectrum sensing and sharing mechanism. In this paper, aiming at achieving a maximal spectrum utilization, we propose a privacy-aware crowdsourced spectrum sensing and multiuser sharing mechanism for DSA. Particularly, in the sensing stage, the advanced mobile crowd sensing is adapted to economically provide sufficient candidate sensing helpers for a sensing requestor. Considering the individual rationality and energy consumption, an incentive mechanism based on both monetary and social motivation is designed to motivate the finial participations of the sensing helpers. Moreover, with the increasing attention to individual privacy, a social network and location-based k-anonymity grouping algorithm are proposed to prevent each helper's privacy being attacked by the malicious requestor or mobile users. Then, for a sensing requestor, aiming at achieving a target detection performance with minimal payment, a truthful reverse auction-based winning group selection algorithm is designed. Furthermore, in the transmitting stage, a realistic scenario is considered where multiple transmitters may discover the same idle spectrum based on sensing helpers' detections and will transmit data simultaneously. Thus, we model this problem as a potential-game-based multiuser transmission mechanism where all the transmitters act as game players and will jointly adjust their transmission powers to maximize the global throughput. Accordingly, we also take advantage of an improved differential evolution algorithm for obtaining a better equilibrium solution in a decentralized way. Both the theoretical analysis and the simulation results prove the rationality and superiority of our proposed algorithms. INDEX TERMS Privacy aware, mobile crowd sensing, incentive mechanism, dynamic spectrum access, game theory.

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Privacy-preserving crowdsourced spectrum sensing

Cited by 75 publications

References 26 publications

Differentially Private Mechanisms for Budget Limited Mobile Crowdsourcing

Differentially Private Mechanisms for Budget Limited Mobile Crowdsourcing

PriStream: Privacy-preserving distributed stream monitoring of thresholded PERCENTILE statistics

Privacy-Aware Crowdsourced Spectrum Sensing and Multi-User Sharing Mechanism in Dynamic Spectrum Access Networks

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