Differential Privacy for Weighted Network Based on Probability Model

Wang, Yuye; Yang, Jing; Zhang, Jianpei

doi:10.1109/access.2020.2991062

Cited by 10 publications

(8 citation statements)

References 29 publications

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“…A Variational Bayes-Weighted Network Differential Privacy (VB-WNDP) scheme was proposed with consideration of the structural role [30]. VB-WNDP establishes a probability model of weighted network through Variational Bayes.…”

Section: ) Differential Privacymentioning

confidence: 99%

“…Proposition 6. Suppose an adversary possesses full access to a published data that satisfy edge weight unlinkability, the probability of edge weight disclosure, P (w A ) = 1 N −1 for MinSwap and [24], [26], [29], [30] [33]- [35] [51]- [57] [36]- [49] [25], [27], [28], [31], [32], [50] MinSwap * δ-MinSwapX * A is anonymity, U is unlinkability and * indicates partially addressed.…”

Section: B Security Evaluationmentioning

confidence: 99%

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Trace Me If You Can: An Unlinkability Approach for Privacy-Preserving in Social Networks

Chong

Malip

2021

IEEE Access

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Privacy in social networks has been a vast active area of research due to the enormous increase in privacy concerns with social networking services. Social networks contain sensitive information of individuals, which could be leaked due to insecure data sharing. To enable a secure social network data publication, several privacy schemes were proposed and built upon the anonymity of users. In this paper, we incorporate unlinkability in the context of weighted network data publication, which has not been addressed in prior work. Two key privacy models are defined, namely edge weight unlinkability and node unlinkability to obviate the linking of auxiliary information to a targeted individual with high probability. Two new schemes satisfying these unlinkability notions, namely MinSwap and δ-MinSwapX are proposed to address edge weight disclosure, link disclosure and identity disclosure problems in publishing weighted network data. The edge weight is modified based on minimum value change in order to preserve the usefulness and properties of the edge weight data. In addition, edge randomization is performed to minimally modify the structural information of a user. Experimental results on real data sets show that our schemes efficiently achieve data utility preservation and privacy protection simultaneously.

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Section: ) Differential Privacymentioning

confidence: 99%

Section: B Security Evaluationmentioning

confidence: 99%

Trace Me If You Can: An Unlinkability Approach for Privacy-Preserving in Social Networks

Chong

Malip

2021

IEEE Access

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“…Specifically, many researchers have begun to fuse various traditional data collaboration solutions with differential technology so as to pursue more effective data protection capability. Typical fusion solutions include: differential technique with CF [37], differential technique with matrix operations [38], differential technique with coding [39], differential technique with probability theory [40]. However, these resolutions still face the challenges of heavy computational costs especially in big data environment.…”

Section: B Collaboration With Privacy-preservationmentioning

confidence: 99%

Trusted Player Transfer Evaluation for Sport Markets Based on Blockchain and Locality-Sensitive Hashing

Liu

et al. 2021

IEEE Access

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With the wide popularity of various sport items and the continuous prosperity of sport business, player transfer events are becoming more and more frequent, which gives birth to a big player transfer market with attractive attentions of spectators. However, for the purposes of business competition or concerns of privacy disclosure, the multiple parties involved in an identical player transfer event are often reluctant to release the concrete transfer fees of a player as the transfer fee is always sensitive enough for the player himself or herself as well as the involved multiple sport clubs. The hidden player transfer fees block the fair and transparent sport business trades and cheat prospective spectators to some extent, which decreases the spectator trust towards player market value and finally, lowers the spectators' satisfaction towards sport business. In this situation, it is becoming a necessity to develop a trusted player transfer evaluation method supported by all the parties involved in player transfer events. Inspired by this challenging task, a trusted player transfer evaluation method with privacy protection is put forward in this paper, which is mainly based on Locality-Sensitive Hashing (LSH) and Blockchain technologies and named TPTELSH+B. At last, a series of simulated experiments are deployed to validate the feasibility of our proposed TPTELSH+B algorithm. Experimental results report that TPTELSH+B has a good evaluation performance with competitive approaches.

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“…Chen et al [15] presented a method for publishing private synthetic graphs, which preserves the community structure of the original graph without sacrificing the ability to capture global structural properties. Wang et al [16] presented a differential privacy method for weighted network through structuring a private probability model.…”

Section: Differentially Private Networkmentioning

confidence: 99%

Differentially Private Attributed Network Releasing Based on Early Fusion

Wang

Yang

Zhan

2021

Security and Communication Networks

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Vertex attributes exert huge impacts on the analysis of social networks. Since the attributes are often sensitive, it is necessary to seek effective ways to protect the privacy of graphs with correlated attributes. Prior work has focused mainly on the graph topological structure and the attributes, respectively, and combining them together by defining the relevancy between them. However, these methods need to add noise to them, respectively, and they produce a large number of required noise and reduce the data utility. In this paper, we introduce an approach to release graphs with correlated attributes under differential privacy based on early fusion. We combine the graph topological structure and the attributes together with a private probability model and generate a synthetic network satisfying differential privacy. We conduct extensive experiments to demonstrate that our approach could meet the request of attributed networks and achieve high data utility.

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Differential Privacy for Weighted Network Based on Probability Model

Cited by 10 publications

References 29 publications

Trace Me If You Can: An Unlinkability Approach for Privacy-Preserving in Social Networks

Trace Me If You Can: An Unlinkability Approach for Privacy-Preserving in Social Networks

Trusted Player Transfer Evaluation for Sport Markets Based on Blockchain and Locality-Sensitive Hashing

Differentially Private Attributed Network Releasing Based on Early Fusion

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