Probability Indistinguishable: A Query and Location Correlation Attack Resistance Scheme

Zhang, Lei; Ma, Chunguang; Yang, Songtao; Zheng, Xing

doi:10.1007/s11277-017-4833-8

Cited by 10 publications

(2 citation statements)

References 28 publications

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“…In this section, three influence maximization methods, two cascade influence models, and two attack models are discussed for comparison. ree influence maximization methods are degree-based [20], random-based [30], and our ICPM method. For the degree-based method, k nodes with higher degree will be selected.…”

Section: Performance Evaluationsmentioning

confidence: 99%

Individual Attribute and Cascade Influence Capability-Based Privacy Protection Method in Social Networks

Zhang

Shi

Weng

et al. 2022

Security and Communication Networks

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Users can obtain intelligent services by sharing information in social networks. Big data technologies can discover underlying benefits from this information. However, stringent security concern is raised at the same time. The public data can be utilized by adversaries, which will bring dire consequences. In this paper, the influence maximization problem is investigated in a privacy protection environment, which aims to find a subset of secure users that can make the spread of influence maximization and privacy disclosure minimization. At first, in order to estimate the risk level for each user, a Bayesian-based individual privacy risk evaluation model is proposed to rank the individual risk levels. Secondly, as the aim is to measure the influence capability for each user, a cascade influence capability evaluation model is designed to rank the friend influence capability levels. Finally, based on these two factors, a privacy protection method is designed for solving the influence maximization with attack constraint problem. In addition, the comparison experiments show that our method can achieve the goal of influence maximization and privacy disclosure minimization efficiently.

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Section: Performance Evaluationsmentioning

confidence: 99%

Individual Attribute and Cascade Influence Capability-Based Privacy Protection Method in Social Networks

Zhang

Shi

Weng

et al. 2022

Security and Communication Networks

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“…Therefore, the core of the technology based on distortion is to design the best privacy protection algorithm when the attacker has obtained the side information. This algorithm cannot only satisfy the needs of users for the quality of service but also meet the requirements of users to protect privacy [32]. In Ref.…”

Section: Location Privacy Mechanismsmentioning

confidence: 99%

A new grid partitioning technology for location privacy protection

2020

Turk J Elec Eng & Comp Sci

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Nowadays, the location-based service (LBS) has become an essential part of convenient service in people's daily life. However, the untrusted LBS servers can store lots of information about the user, such as the user's identity, location, and destination. Then the information can be used as background knowledge and combined with the query frequency of the user to launch the inference attack to obtain user's privacy. In most of the existing schemes, the author considers the algorithm of virtual location selection from the historical location of the user. However, the LBS server can infer the user's location information on the historical data that has been counted for a long time. In order to ensure that the users' historical query data and query frequency will not be obtained by the attacker, we propose a privacy protection algorithm based on grid expansion. With the help of third-party agents, the combination of cooperative users and pseudonyms can resist the privacy disclosure caused by users requesting services during the mobile process.Extensive simulation experiments have been carried out on Gowalla dataset to evaluate the efficiency of the proposed algorithm. By comparing with other existing methods, the experimental verify the effectiveness of our algorithm in privacy protection.

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Dimension-aware under spatiotemporal constraints: an efficient privacy-preserving framework with peak density clustering

Zhang

Huang

et al. 2022

J Supercomput

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Probability Indistinguishable: A Query and Location Correlation Attack Resistance Scheme

Cited by 10 publications

References 28 publications

Individual Attribute and Cascade Influence Capability-Based Privacy Protection Method in Social Networks

Individual Attribute and Cascade Influence Capability-Based Privacy Protection Method in Social Networks

A new grid partitioning technology for location privacy protection

Dimension-aware under spatiotemporal constraints: an efficient privacy-preserving framework with peak density clustering

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