scholarly journals Publishing Triangle Counting Histogram in Social Networks Based on Differential Privacy

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Tianzi Lv ◽  
Huanzhou Li ◽  
Zhangguo Tang ◽  
Fangzhou Fu ◽  
Jian Cao ◽  
...  
Keyword(s):  
Social Network ◽  
Differential Privacy ◽  
Data Availability ◽  
Projection Algorithm ◽  
Published Data ◽  
Network Data ◽  
Original Graph ◽  
Social Network Data ◽  
Protection Method ◽  
Triangle Counting

The continuous expansion of the number and scale of social networking sites has led to an explosive growth of social network data. Mining and analyzing social network data can bring huge economic value and social benefits, but it will result in privacy leakage and other issues. The research focus of social network data publishing is to publish available data while ensuring privacy. Aiming at the problem of low data availability of social network node triangle counting publishing under differential privacy, this paper proposes a privacy protection method of edge triangle counting. First, an edge-removal projection algorithm TSER based on edge triangle count sorting is proposed to obtain the upper bound of sensitivity. Then, two edge triangle count histogram publishing methods satisfying edge difference privacy are given based on the TSER algorithm. Finally, experimental results show that compared with the existing algorithms, the TSER algorithm can retain more triangles in the original graph, reduce the error between the published data and the original data, and improve the published data availability.

Privacy Preserving Approaches for Online Social Network Data Publishing

2021 ◽  
pp. 119-132
Author(s):  
Kamalkumar Macwan ◽  
Sankita Patel
Keyword(s):  
Social Network ◽  
Privacy Preservation ◽  
Differential Privacy ◽  
Online Social Network ◽  
Data Publishing ◽  
Network Data ◽  
User Privacy ◽  
Social Network Data ◽  
Huge Data ◽  
Popularity Prediction

Recently, the social network platforms have gained the attention of people worldwide. People post, share, and update their views freely on such platforms. The huge data contained on social networks are utilized for various purposes like research, market analysis, product popularity, prediction, etc. Although it provides so much useful information, it raises the issue regarding user privacy. This chapter discusses the various privacy preservation methods applied to the original social network dataset to preserve privacy against attacks. The two areas for privacy preservation approaches addressed in this chapter are anonymization in social network data publication and differential privacy in node degree publishing.

scholarly journals A Privacy Protection Method for Social Network Data against Content/Degree Attacks

2012 ◽  
Vol E95-D (1) ◽  
pp. 152-160
Author(s):  
Min Kyoung SUNG ◽  
Ki Yong LEE ◽  
Jun-Bum SHIN ◽  
Yon Dohn CHUNG
Keyword(s):  
Social Network ◽  
Privacy Protection ◽  
Network Data ◽  
Social Network Data ◽  
Protection Method

Partial k-Anonymity for Privacy-Preserving Social Network Data Publishing

2017 ◽  
Vol 27 (01) ◽  
pp. 71-90 ◽  
Author(s):  
Peng Liu ◽  
Yan Bai ◽  
Lie Wang ◽  
Xianxian Li
Keyword(s):  
Social Network ◽  
Data Privacy ◽  
Privacy Preserving ◽  
Data Publishing ◽  
Published Data ◽  
Network Data ◽  
Social Network Data ◽  
Label Information ◽  
Real World Datasets ◽  
Randomization Algorithms

With the popularity of social networks, privacy issues with regard to publishing social network data have gained intensive focus from academia. We analyzed the current privacy-preserving techniques for publishing social network data and defined a privacy-preserving model with privacy guarantee [Formula: see text]. With our definitions, the existing privacy-preserving methods, [Formula: see text]-anonymity and randomization can be combined together to protect data privacy. We also considered the privacy threat with label information and modify the [Formula: see text]-anonymity technique of tabular data to protect the published data from being attacked by the combination of two types of background knowledge, the structural and label knowledge. We devised a partial [Formula: see text]-anonymity algorithm and implemented it in Python and open source packages. We compared the algorithm with related [Formula: see text]-anonymity and random techniques on three real-world datasets. The experimental results show that the partial [Formula: see text]-anonymity algorithm preserves more data utilities than the [Formula: see text]-anonymity and randomization algorithms.

Abstract #1249: Novel Use of Physician Social Network Data to Map the Gap in the Endocrinology Workforce

2015 ◽  
Vol 21 ◽  
pp. 301
Author(s):  
Armand Krikorian ◽  
Lily Peng ◽  
Zubair Ilyas ◽  
Joumana Chaiban
Keyword(s):  
Social Network ◽  
Network Data ◽  
Social Network Data

The Multilevel p2 Model

Methodology ◽  
2006 ◽  
Vol 2 (1) ◽  
pp. 42-47 ◽  
Author(s):  
Bonne J. H. Zijlstra ◽  
Marijtje A. J. van Duijn ◽  
Tom A. B. Snijders
Keyword(s):  
Social Network ◽  
Network Data ◽  
Mcmc Algorithm ◽  
Independent Observation ◽  
Single Observation ◽  
Multiple Networks ◽  
Social Network Data ◽  
Multiple Observations ◽  
Practical Support ◽  
The Social

The p 2 model is a random effects model with covariates for the analysis of binary directed social network data coming from a single observation of a social network. Here, a multilevel variant of the p 2 model is proposed for the case of multiple observations of social networks, for example, in a sample of schools. The multilevel p 2 model defines an identical p 2 model for each independent observation of the social network, where parameters are allowed to vary across the multiple networks. The multilevel p 2 model is estimated with a Bayesian Markov Chain Monte Carlo (MCMC) algorithm that was implemented in free software for the statistical analysis of complete social network data, called StOCNET. The new model is illustrated with a study on the received practical support by Dutch high school pupils of different ethnic backgrounds.

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