Collective Data-Sanitization for Preventing Sensitive Information Inference Attacks in Social Networks

Cai, Zhipeng; He, Zaobo; Guan, Xinping; Li, Yingshu

doi:10.1109/tdsc.2016.2613521

Cited by 357 publications

(225 citation statements)

References 21 publications

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“…For example, whether two individuals in a social network have a close relationship may be expected to be kept a secret. Therefore, privacy concerns have been raised in increasingly emerging technologies [2][3][4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

An Edge Correlation Based Differentially Private Network Data Release Method

Cai

Wang

et al. 2017

Security and Communication Networks

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Differential privacy (DP) provides a rigorous and provable privacy guarantee and assumes adversaries' arbitrary background knowledge, which makes it distinct from prior work in privacy preserving. However, DP cannot achieve claimed privacy guarantees over datasets with correlated tuples. Aiming to protect whether two individuals have a close relationship in a correlated dataset corresponding to a weighted network, we propose a differentially private network data release method, based on edge correlation, to gain the tradeoff between privacy and utility. Specifically, we first extracted the Edge Profile (PF) of an edge from a graph, which is transformed from a raw correlated dataset. Then, edge correlation is defined based on the PFs of both edges via JensonShannon Divergence (JS-Divergence). Secondly, we transform a raw weighted dataset into an indicated dataset by adopting a weight threshold, to satisfy specific real need and decrease query sensitivity. Furthermore, we propose -correlated edge differential privacy (CEDP), by combining the correlation analysis and the correlated parameter with traditional DP. Finally, we propose network data release (NDR) algorithm based on the -CEDP model and discuss its privacy and utility. Extensive experiments over real and synthetic network datasets show the proposed releasing method provides better utilities while maintaining privacy guarantee.

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Section: Introductionmentioning

confidence: 99%

An Edge Correlation Based Differentially Private Network Data Release Method

Cai

Wang

et al. 2017

Security and Communication Networks

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“…Chen et al 24 study the problem of influence maximization under topic-aware applications. Cai et al 25 use the idea of information diffusion to prevent sensitive information in social networks. More related work on applications in social networks can be found in the works of Han et al 26 and Bi et al 27 Although the problem of extending classic influence maximization methods has been studied by many research works as shown above, we are not aware of any efforts on influence maximization on dynamic setting.…”

Section: Related Workmentioning

confidence: 99%

Diffusing information for mobile social networks under consideration of dynamic influence

Jing

Liu

2017

International Journal of Distributed Sensor Networks

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As the developments of new techniques, mobile social networks have been built wildly. To obtain and spread information over mobile social networks efficiently, the influence maximization problem is to find a seed nodes set with limited size such that it can influence as many nodes as possible. Previous works ignore the dynamic influence phenomenon of diffusing information on mobile social networks. In this article, we propose a new model to express the procedure of diffusing information under the existence of dynamic influence. Theoretical analysis shows that the influence maximization problem under new model is non-deterministic polynomial-time hard, and efficient approximation algorithm is proposed. Experimental studies on real data sets show that the new model can process dynamic influence well in the diffusing information procedure, and the proposed algorithms can solve the influence maximization problem on new model efficiently.

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“…While people are enjoying the many benefits brought by mobile devices, people have to take the risk of losing privacy by leaking private information [1][2][3][4][5][6][7][8], especially location information [9][10][11][12][13]. People now heavily rely on services provided by third-party Apps which usually collect users' location information.…”

Section: Introductionmentioning

confidence: 99%

Location Privacy Leakage through Sensory Data

Liang

Cai

Han

et al. 2017

Security and Communication Networks

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Mobile devices bring benefits as well as the risk of exposing users' location information, as some embedded sensors can be accessed without users' permission and awareness. In this paper, we show that, only by using the data collected from the embedded sensors in mobile devices instead of GPS data, we can infer a user's location information with high accuracy. Three issues are addressed which are route identification, user localization in a specific route, and user localization in a bounded area. The Dynamic Time Warping based technique is designed and we develop a Hidden Markov Model to solve the localization problem. Real experiments are performed to evaluate our proposed methods.

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Collective Data-Sanitization for Preventing Sensitive Information Inference Attacks in Social Networks

Cited by 357 publications

References 21 publications

An Edge Correlation Based Differentially Private Network Data Release Method

An Edge Correlation Based Differentially Private Network Data Release Method

Diffusing information for mobile social networks under consideration of dynamic influence

Location Privacy Leakage through Sensory Data

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