Privacy-Preserving Triangle Counting in Large Graphs

Ding, Xiuli; Zhang, Xiaodong; Bao, Zhifeng; Jin, Hai

doi:10.1145/3269206.3271736

Cited by 37 publications

(34 citation statements)

References 27 publications

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“…These valuable statistics can be utilized for range of applications including social network analysis, marketing, preference mining and analysis, collaborative filtering, epidemiological investigation, and information spread/contagion etc. A sample of the graphs' statistics computed with the help of node DP techniques [118], [119] and minimum spanning tree (MST) DP [120] approaches is presented in Figure 14.…”

Section: Structural Anonymization Techniques Used For the Social Nmentioning

confidence: 99%

Anonymization Techniques for Privacy Preserving Data Publishing: A Comprehensive Survey

2021

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Section: Structural Anonymization Techniques Used For the Social Nmentioning

confidence: 99%

Anonymization Techniques for Privacy Preserving Data Publishing: A Comprehensive Survey

2021

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“…In DFG, frequent subgraphs are privately identified in the first phase, and the noisy support of each identified frequent subgraph is calculated in the second phase. Ding et al [11] published the triangle counts satisfying the node-differential privacy with two kinds of histograms: the triangle count distribution and the cumulative distribution. Sun et al [12] studied fundamental problems related to extended local view.…”

Section: Related Workmentioning

confidence: 99%

Differential Privacy for Weighted Network Based on Probability Model

2020

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Weighted network contains a lot of sensitive information and may seriously jeopardize individual privacy. In this paper, we study the problem of differential privacy for weighted network. We found most existing methods add noise to edge weights directly and neglect the structural role of node. These methods perform with low accuracy. To address the above issue, we propose two approaches. One approach describes a differential privacy method for Stochastic Block Model. This private SBM reveals and the structural role of node and respects the privacy of it. Another approach develops a differential privacy method for weighted network through structuring a private probability model. We use Variational Bayes to learn the private model parameters. It adds noise to the parameters of the probability model instead of edge weights, and achieve high data utility. Experiments on real datasets illustrate that our algorithm privately releases weighted network and achieves high accuracy. INDEX TERMS Weighted network, differential privacy, stochastic block model, variational bayes.

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“…This algorithm cannot adapt to different datasets because of its fixed grouping. Ding et al [16] proposed a node-differential privacy algorithm for each node 3-subgraph counting is used for protection, and the algorithm was extended to the local clustering coefficient; Qian et al [15] proposed two algorithms to publish the node strength histogram of graphs under edge-differential privacy for the first time, in which the node strength histogram publishing algorithm based on sequence awareness has high accuracy but high time overhead, and the privacy protection provided by edge-differential privacy is weaker than that of nodedifferential privacy.…”

Section: Related Workmentioning

confidence: 99%

“…Next, the real value of each bucket in the group is replaced by the average of the group. Finally, add noise to release(lines [16][17][18].…”

Section: Algorithm 2: Alt-histogram Algorithmmentioning

confidence: 99%

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Publishing Node Strength Distribution With Node Differential Privacy

Liu

2020

IEEE Access

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The challenge of graph data publishing under node-differential privacy mainly comes from the high sensitivity of the query. Compared with edge-differential privacy that can only protect the relationship between people, node-differential privacy can protect both the relationship between people and personal information. Therefore, Node-differential privacy must pay attention to the protection of personal information. This paper studies the release of node strength distribution under node-differential privacy by reducing sensitivity. We propose two algorithms to publish node strength distribution: Alternate-histogram(ALThistogram) and Density-histogram(DEN-histogram). Experimental results show that compared with the existing node strength histogram publishing algorithm, our proposed algorithm has advantages in L1-error and KS-distance, make the noise node strength distribution closer to that of the original graph. We also propose an introspective analysis to understand the influence of the projection algorithm on the node strength distribution, experiments to prove that the projection algorithm plays an important role in the node strength distribution.

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Privacy-Preserving Triangle Counting in Large Graphs

Cited by 37 publications

References 27 publications

Anonymization Techniques for Privacy Preserving Data Publishing: A Comprehensive Survey

Anonymization Techniques for Privacy Preserving Data Publishing: A Comprehensive Survey

Differential Privacy for Weighted Network Based on Probability Model

Publishing Node Strength Distribution With Node Differential Privacy

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