Collecting and analyzing massive data generated from smart devices have become increasingly pervasive in crowdsensing, which are the building blocks for data-driven decision-making. However, extensive statistics and analysis of such data will seriously threaten the privacy of participating users. Local differential privacy (LDP) was proposed as an excellent and prevalent privacy model with distributed architecture, which can provide strong privacy guarantees for each user while collecting and analyzing data. LDP ensures that each user’s data is locally perturbed first in the client-side and then sent to the server-side, thereby protecting data from privacy leaks on both the client-side and server-side. This survey presents a comprehensive and systematic overview of LDP with respect to privacy models, research tasks, enabling mechanisms, and various applications. Specifically, we first provide a theoretical summarization of LDP, including the LDP model, the variants of LDP, and the basic framework of LDP algorithms. Then, we investigate and compare the diverse LDP mechanisms for various data statistics and analysis tasks from the perspectives of frequency estimation, mean estimation, and machine learning. Furthermore, we also summarize practical LDP-based application scenarios. Finally, we outline several future research directions under LDP.

With the tremendous pervasion of location-based services (LBS) in vehicular networks, the location privacy of vehicles has become an utmost concern. K-anonymity is one of the most popular privacy protection solutions, in which the real location of the request vehicle (RV) can be covered by a cloaking area including the location of k-1 cooperative vehicles (CVs). However, K-anonymity assumes all CVs are always honest and thus offering opportunities for dishonest CVs to provide false location information. To combat such threats, we propose a trusted cloaking area construction (TCAC) scheme based on the trust mechanism to protect the location privacy of vehicles. In this article, the trust value is not only used to identify dishonest CVs, but also utilized to decide the LBS request of RV. A low trust value will make the LBS request be rejected due to dishonest and selfish cooperative behaviors when the RV has played the role of CV. To deal with the massive trust requirements caused by frequent vehicle movement, edge computing is employed to assist trust value evaluation. Moreover, traditional central and distributed trust data management may be unsuitable for vehicular networks. We also propose a blockchain-based trust data management method by combining vehicular regions partition, so as to rapidly evaluate trust value during the cloaking area construction. The security analysis and simulation results indicate that our proposed scheme is resilient to suppress dishonest CVs, inspire selfish CVs, and protect the location privacy of vehicles effectively, whereas the required computation time and communication cost are both limited.

Cooperative spectrum sensing (CSS) is considered as a powerful approach to improve the utilization of scarce spectrum resources. However, CSS assumes that all secondary users (SU) always are honest, thus offering opportunities for attackers to launch spectrum sensing data falsification (SSDF) attack. To combat such threat, recent efforts have been made to trust mechanism. In this paper, we argue that powering CSS with trust mechanism is not enough. A vulnerability for trust mechanism is found, that is, the feedback data from initiator SUs are generally unchecked. Such feedback data corresponding to the spectrum status of primary users can be exploited by attackers to disturb trust mechanism in a collusive manner. Especially, collusive false feedback (CFF) attackers would form a clique to help each other. One of CFF attackers can disguise as an initiator SU who sends the feedback in accordance with the sensing data of their conspirators who play the role of cooperating SUs, resulting in promoting their conspirators' sensing trust quickly. With high sensing trust, attackers can avoid the detection of trust mechanism and, hence, launch SSDF attack (fake sensing data) more easily. We conduct an in-depth investigation on CFF attack and propose a two-level defense scheme called FeedGuard from the design ideas of feedback trust and I-C frequency correlation analysis. Simulation results show that the FeedGuard scheme can enhance the accuracy of sensing trust evaluation, thus successfully reducing the power of CFF attack against CSS.

Cooperative spectrum sensing (CSS) is considered as a powerful approach to improve the utilization of scarce spectrum resources. However, if CSS assumes that all secondary users (SU) are honest, it may offer opportunities for attackers to conduct a spectrum sensing data falsification (SSDF) attack. To suppress such a threat, recent efforts have been made to develop trust mechanisms. Currently, some attackers can collude with each other to form a collusive clique, and thus not only increase the power of SSDF attack but also avoid the detection of a trust mechanism. Noting the duality of sensing data, we propose a defense scheme called XDA from the perspective of XOR distance analysis to suppress a collusive SSDF attack. In the XDA scheme, the XOR distance calculation in line with the type of “0” and “1” historical sensing data is used to measure the similarity between any two SUs. Noting that collusive SSDF attackers hold high trust value and the minimum XOR distance, the algorithm to detect collusive SSDF attackers is designed. Meanwhile, the XDA scheme can perfect the trust mechanism to correct collusive SSDF attackers’ trust value. Simulation results show that the XDA scheme can enhance the accuracy of trust evaluation, and thus successfully reduce the power of collusive SSDF attack against CSS.

Cooperative spectrum sensing (CSS) has been recognized as a forceful approach to promote the utilization of spectrum bands. Nevertheless, all secondary users (SU) are assumed as honest in CSS, thus giving opportunities for attackers to launch the spectrum sensing data falsification (SSDF) attack. To defend against such attack, many efforts have been made to trust mechanism. In this paper, we argue that securing CSS with only trust mechanism is not enough and report the description of dynamic-collusive SSDF attack (DC-SSDF attack). To escape the detection of trust mechanism, DC-SSDF attackers can maintain high trust by submitting true sensing data dynamically and then fake sensing data in the collaborative manner to increase their attack strength. Noting that the resonance phenomenon may appear in the trust value curve of DC-SSDF attackers, a defense scheme called TFCA is proposed from the design idea of trust fluctuation clustering analysis to suppress DC-SSDF attack. In the TFCA scheme, the decreasing property of trust value in the resonance phenomenon is adopted to measure the similarity distance between two attackers. Based on the similarity distance computation, the binary clustering algorithm is designed by electing initial binary samples to identify DC-SSDF attackers. Finally, trust mechanism can be perfected by TFCA to correct DC-SSDF attackers’ trust value. Simulation results show that our TFCA scheme can improve the accuracy of trust value calculation, thus reducing the strength of DC-SSDF attack successfully.

Traffic-related messages exchange (TME) is considered as a powerful approach to improve traffic safety and efficiency in vehicular networks. However, TME assumes all vehicles always are honest, and thus offering opportunities for attackers to fake traffic-related messages. To combat such threat, recent efforts have been made to trust mechanism. In this paper, a vulnerability for trust mechanism is found, that is, the ratings from initiator vehicles are generally unchecked. Such ratings corresponding to the truth of traffic-related events can be exploited by attackers to disturb trust mechanism. Specially, attackers would form a clique to help with each other in an inside-and-outside collusive (IOC) manner. One of IOC attackers can disguise as an initiator vehicle who sends the rating in accordance with the traffic-related messages of his conspirators, result in promoting their trust value quickly. With high trust value, attackers can escape the detection of trust mechanism. We conduct an in-depth investigation on IOC attack and propose a defense scheme called TFAA from the design ideas of trust fluctuation association analysis. In addition, the trust data management of central and distributed trust mechanism may be unsuitable for vehicular networks. To support the trust data management for the TFAA scheme, we also design a semi-distributed trust data storage scheme called TruChain with the combination of consortium blockchain and vehicular regions partition. Simulation results show that the TFAA scheme can enhance the accuracy of trust value evaluation, and thus successfully reducing the power of IOC attack against TME.