Local business service systems (LBSS), as an essential role of location-based service (LBS), have been gaining tremendous popularity in our daily life. Individuals' reviews in these systems are very important as they not only contribute to building reputations for businesses but also play a guiding role for consumers. However, users' privacy disclosure and the effectiveness of reviews are the urgent problems to be solved for the further development of LBSS. This paper proposes a mechanism to improve effectiveness and privacy preservation for review publication. In users' privacy protection, the mechanism firstly formalizes the model of attackers, then focuses on the identification or inference attack caused by reviews. For improving the effectiveness of reviews, the mechanism introduces users' reputation scores to rank the reviews. We evaluate our mechanism thoroughly by extensive experiments, and the results validate that our mechanism can achieve a better performance.INDEX TERMS Location-based service (LBS), privacy protection, review publication.
With the rapid development of smart handheld devices, wireless communication, and positioning technologies, location-based service (LBS) has been gaining tremendous popularity in mobile social networks (MSN). Users’ daily lives are facilitated by the applications of LBS, but users’ privacy leaking hinders the further development of LBS. In order to solve this problem, techniques such as k-anonymity and l-diversity have been widely adopted. However, most papers that combine with k-anonymity and l-diversity focus on the security of users’ privacy with little consideration of service efficiency. In this paper, we firstly treat the relationship between k-anonymity and l-diversity in the clustering process from a dynamic and global perspective. Then a service category table based algorithm (SCTB) is designed to identify and calculate l-diversity securely and efficiently, which promotes the cooperative efficiency of users in LBS query, especially when the preference privacy that users request in the clustering process have similarities. Finally, theoretical performance analysis and extensive experimental studies are performed to validate the effectiveness of our SCTB algorithm.
With the increasing convenience of location-based services (LBSs), there have been growing concerns about the risk of privacy leakage. We show that existing techniques fail to defend against a statistical attack meant to infer the user’s location privacy and query privacy, which is due to continuous queries that the same user sends in the same location in a short time, causing the user’s real location to appear consecutively more than once and the query content to be the same or similar in the neighboring query. They also fail to consider the hierarchical structure of the address, so locations in an anonymous group may be located in the same organization, resulting in leaking of the user’s organization information and reducing the privacy protection effect. This paper presents a dummy generation scheme, considering the hierarchical structure of the address (DGS-HSA). In our scheme, we introduce a novel meshing method, which divides the historical location dataset according to the administrative region division. We also choose dummies from the historical location dataset with the two-level grid structure to realize the protection of the user’s location, organization information, and query privacy. Moreover, we prove the feasibility of the presented scheme by solving the multi-objective optimization problem and give the user’s privacy protection parameters recommendation settings, which balance the privacy protection level and system overhead. Finally, we evaluate the effectiveness and the correctness of the DGS-HSA through theoretical analysis and extensive simulations.
Abstract-At present, development of science and technology accelerates the society-informationization, many enterprises follow the trend of era to build internal network for convenient communication, but the increasing network security incidents cause a new understanding about the importance of internal network. The predictive model of insider threat based on Bayesian network is put forward in this paper. In the model, insider behaviors in the process of operation are considered as research objects, resource and intrusion evidence for operation sequence are seen as nodes, and then the network attack graph of Bayesian network is established. The concept of meta-operation, atomic attack and intrusion evidence are put forward in the graph. The node variable, its value and the conditional probability distribution of network attack graph are defined. Based on Bayesian network approximate inference, the improved likelihood weighted algorithm is presented to calculate the parameter and to quantify the insider threat. According to the simulation experiment data analysis, this approach is fast, simple and accurate, and plays an effective role in the process of insider threat prediction and evaluation.
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