A trusted de-swinging k-anonymity scheme for location privacy protection

K-anonymity has been gaining widespread attention as one of the most widely used technologies to protect location privacy. Nevertheless, there are still some threats such as behavior deception and service swing, since utilizing distributed k-anonymity technology to construct an anonymous domain. More specifically, the coordinate of the honest node will be a leak if the malicious nodes submit wrong locations coordinate to take part in the domain construction process. Worse still, owing to service swing, the attacker increases the reputation illegally to deceive honest nodes again. To overcome those drawbacks, we propose a trusted de-swinging k-anonymity scheme for location privacy protection. Primarily, we introduce a de-swinging reputation evaluation method (DREM), which designs a penalty factor to curb swinging behavior. This method calculates the reputation from entity honesty degree, location information entropy, and service swing degree. Besides, based on our proposed DREM, a credible cloaking area is constructed to protect the location privacy of the requester. In the area, nodes can choose some nodes with a high reputation for completing the construction process of the anonymous domain. Finally, we design reputation contracts to calculate credit automatically based on smart contracts. The security analysis and simulation results indicate that our proposed scheme effectively resists malicious attacks, curbs the service swing, and encourages nodes to participate honestly in the construction of cloaking areas.

Anonymous Mutual and Batch Authentication with Location Privacy of UAV in FANET

As there has been an advancement in avionic systems in recent years, the enactment of unmanned aerial vehicles (UAV) has upgraded. As compared to a single UAV system, multiple UAV systems can perform operations more inexpensively and efficiently. As a result, new technologies between user/control station and UAVs have been developed. FANET (Flying Ad-Hoc Network) is a subset of the MANET (Mobile Ad-Hoc Network) that includes UAVs. UAVs, simply called drones, are used for collecting sensitive data in real time. The security and privacy of these data are of priority importance. Therefore, to overcome the privacy and security threats problem and to make communication between the UAV and the user effective, a competent anonymous mutual authentication scheme is proposed in this work. There are several methodologies addressed in this work such as anonymous batch authentication in FANET which helps to authenticate a large group of drones at the same time, thus reducing the computational overhead. In addition, the integrity preservation technique helps to avoid message alteration during transmission. Moreover, the security investigation section discusses the resistance of the proposed work against different types of possible attacks. Finally, the proposed work is related to the prevailing schemes in terms of communication and computational cost and proves to be more efficient.

A Privacy Preserved, Trust Relationship (PTR) Model for Internet of Vehicles

The Internet of vehicles (IoV) depicts a reality where ordinary things are connected to vehicular ad-hoc networks (VANETs), allowing them to transmit and collaborate. By placing these regular objects in VANETs and making them available at any time, this network and data sharing may raise real privacy and security issues. Thus, group-based communication is mostly preferred in the literature. However, in heavy network scenarios, cluster-based communication mostly leads to additional overload in the form of the group leader that causes delay and disrupts the performance of a network. Due to the interaction of VANETs with applications that are not stable for life, privacy and security mechanism for detecting many malicious nodes is in great demand. Therefore, a multi-phantom node selection has been proposed in this paper to select trustworthy, normal, and malicious nodes. The multi-phantom node scheme is proposed to reduce the phantom node load, where the multi-lateral nodes in a cluster act as a phantom node to share the load. A multi criteria decision-making (MCDM) methodology (analytic network process) is used to optimize the phantom node to pre-serve privacy using the privacy preserved trust relationship (PTR) model. The results show checking the stability of parameters and using sensitivity analysis by considering different scenarios for the most optimal phantom node to preserve vehicle location privacy. The impact of the proposed model will be more clearly visible in its real-time implementation in urban areas vehicle networks.

KLPPS: A k-Anonymous Location Privacy Protection Scheme via Dummies and Stackelberg Game

Protecting location privacy has become an irreversible trend; some problems also come such as system structures adopted by location privacy protection schemes suffer from single point of failure or the mobile device performance bottlenecks, and these schemes cannot resist single-point attacks and inference attacks and achieve a tradeoff between privacy level and service quality. To solve these problems, we propose a k-anonymous location privacy protection scheme via dummies and Stackelberg game. First, we analyze the merits and drawbacks of the existing location privacy preservation system architecture and propose a semitrusted third party-based location privacy preservation architecture. Next, taking into account both location semantic diversity, physical dispersion, and query probability, etc., we design a dummy location selection algorithm based on location semantics and physical distance, which can protect users’ privacy against single-point attack. And then, we propose a location anonymous optimization method based on Stackelberg game to improve the algorithm. Specifically, we formalize the mutual optimization of user-adversary objectives by using the framework of Stackelberg game to find an optimal dummy location set. The optimal dummy location set can resist single-point attacks and inference attacks while effectively balancing service quality and location privacy. Finally, we provide exhaustive simulation evaluation for the proposed scheme compared with existing schemes in multiple aspects, and the results show that the proposed scheme can effectively resist the single-point attack and inference attack while balancing the service quality and location privacy.

On Location Privacy Protection Algorithm Based on Edge Computing and Caching

To resolve the communication overhead problem of anonymous users, we propose a location privacy protection method based on the cache technology. In particular, we first place the cache center on edge server nodes to reduce interaction between servers and users. In this way, the risk of privacy leaks can be reduced. Furthermore, to improve the caching hit rate, a prediction system based on Markov chain is designed to protect the trajectory privacy of mobile users. Simulations show that the algorithm can protect the privacy of users and reduce the transmission delay.