Vehicular ad hoc networks (VANETs) are becoming the most promising research topic in intelligent transportation systems, because they provide information to deliver comfort and safety to both drivers and passengers. However, unique characteristics of VANETs make security, privacy, and trust management challenging issues in VANETs’ design. This survey article starts with the necessary background of VANETs, followed by a brief treatment of main security services, which have been well studied in other fields. We then focus on an in-depth review of anonymous authentication schemes implemented by five pseudonymity mechanisms. Because of the predictable dynamics of vehicles, anonymity is necessary but not sufficient to thwart tracking an attack that aims at the drivers’ location profiles. Thus, several location privacy protection mechanisms based on pseudonymity are elaborated to further protect the vehicles’ privacy and guarantee the quality of location-based services simultaneously. We also give a comprehensive analysis on various trust management models in VANETs. Finally, considering that current and near-future applications in VANETs are evaluated by simulation, we give a much-needed update on the latest mobility and network simulators as well as the integrated simulation platforms. In sum, this paper is carefully positioned to avoid overlap with existing surveys by filling the gaps and reporting the latest advances in VANETs while keeping it self-explained.

A Survey on Recent Advances in Vehicular Network Security, Trust, and Privacy

A caching and spatial K-anonymity driven privacy enhancement scheme in continuous location-based services

Efficient location privacy algorithm for Internet of Things (IoT) services and applications

The rise of e-commerce has not only given consumers more choice but has also caused information overload. In order to quickly find favorite items from vast resources, users are eager for technology by which websites can automatically deliver items in which they may be interested. Thus, recommender systems are created and developed to automate the recommendation process. In the field of collaborative filtering recommendations, the accuracy requirement of the recommendation algorithm always makes it complex and difficult to implement one algorithm. The slope one algorithm is not only easy to implement but also works efficient and effective. However, the prediction accuracy of the slope one algorithm is not very high. Moreover, the slope one algorithm does not perform so well when dealing with personalized recommendation tasks that concern the relationship among users. To solve these problems, we propose a slope one algorithm based on the fusion of trusted data and user similarity, which can be deployed in various recommender systems. This algorithm comprises three procedures. First, we should select trusted data. Second, we should calculate the similarity between users. Third, we need to add this similarity to the weight factor of the improved slope one algorithm, and then, we get the final recommendation equation. We have carried out a number of experiments with the Amazon dataset, and the results prove that our recommender algorithm performs more accurately than the traditional slope one algorithm.

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A trust-based collaborative filtering algorithm for E-commerce recommendation system

With the development of wireless communication and positioning technology, location-based services (LBSs) have been gaining tremendous popularity, due to its ability to greatly facilitate the people’s daily lives. Meanwhile, it also entails the risk of location privacy disclosure. To address this issue, general solutions introduce a single trusted anonymizer between the users and the location service provider (LSP). However, a single anonymizer offers limited privacy guarantees and incurs high communication overhead in continuous LBSs. Once the anonymizer is compromised, it may put the user information in jeopardy. In this paper, we propose a dual privacy preserving (DPP) scheme in continuous LBSs to protect the users’ trajectory and query privacy. Our scheme introduces multiple anonymizers between the users and LSP, and combines with Shamir threshold mechanism, dynamic pseudonym mechanism, and ${K}$ -anonymity technology to improve the users’ trajectory and content privacy in continuous LBSs. An anonymizer alone cannot get the users’ trajectory and query contents, and it thus can be semi-trusted. Our scheme can enhance the users’ privacy and effectively solve the single point of failure in single anonymizer structure. At the same time, the query authentication can guarantee the correctness of the query results. The analysis and simulation results demonstrate that the proposed scheme has the ability to protect users’ trajectory and content privacy effectively, and to reduce the computation and communication overhead of the single anonymizer.

Dacheng Meng

Papers

Collaborative trajectory privacy preserving scheme in location-based services