scholarly journals The Study of Data Publishing Technology based on the Differential Privacy in Social Networks

2016 ◽  
Author(s):  
Nan Ning ◽  
Changlun Zhang ◽  
Zhanyong Jin ◽  
Zhan Yu
Keyword(s):  
Social Networks ◽  
Differential Privacy ◽  
Data Publishing ◽  
Publishing Technology

scholarly journals Research on Privacy Protection Technology of Mobile Social Network Based on Data Mining under Big Data

2022 ◽  
Vol 2022 ◽  
pp. 1-9
Author(s):  
Jiawen Du ◽  
Yong Pi
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Big Data ◽  
Private Information ◽  
Privacy Protection ◽  
Differential Privacy ◽  
Study Data ◽  
Data Publishing ◽  
Data Mining Algorithm ◽  
Data Mining Algorithms

With the advent of the era of big data, people’s lives have undergone earth-shaking changes, not only getting rid of the cumbersome traditional data collection but also collecting and sorting information directly from people’s footprints on social networks. This paper explores and analyzes the privacy issues in current social networks and puts forward the protection strategies of users’ privacy data based on data mining algorithms so as to truly ensure that users’ privacy in social networks will not be illegally infringed in the era of big data. The data mining algorithm proposed in this paper can protect the user’s identity from being identified and the user’s private information from being leaked. Using differential privacy protection methods in social networks can effectively protect users’ privacy information in data publishing and data mining. Therefore, it is of great significance to study data publishing, data mining methods based on differential privacy protection, and their application in social networks.

scholarly journals Differentially Private Autocorrelation Time-Series Data Publishing Based on Sliding Window

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jing Zhao ◽  
Shubo Liu ◽  
Xingxing Xiong ◽  
Zhaohui Cai
Keyword(s):  
Time Series ◽  
Privacy Protection ◽  
Large Scale ◽  
Differential Privacy ◽  
Time Series Data ◽  
Sliding Window ◽  
Data Publishing ◽  
Series Data ◽  
Data Publication ◽  
Autocorrelation Time

Privacy protection is one of the major obstacles for data sharing. Time-series data have the characteristics of autocorrelation, continuity, and large scale. Current research on time-series data publication mainly ignores the correlation of time-series data and the lack of privacy protection. In this paper, we study the problem of correlated time-series data publication and propose a sliding window-based autocorrelation time-series data publication algorithm, called SW-ATS. Instead of using global sensitivity in the traditional differential privacy mechanisms, we proposed periodic sensitivity to provide a stronger degree of privacy guarantee. SW-ATS introduces a sliding window mechanism, with the correlation between the noise-adding sequence and the original time-series data guaranteed by sequence indistinguishability, to protect the privacy of the latest data. We prove that SW-ATS satisfies ε-differential privacy. Compared with the state-of-the-art algorithm, SW-ATS is superior in reducing the error rate of MAE which is about 25%, improving the utility of data, and providing stronger privacy protection.

Privacy-preserving governmental data publishing: A fog-computing-based differential privacy approach

2019 ◽  
Vol 90 ◽  
pp. 158-174 ◽  
Author(s):  
Chunhui Piao ◽  
Yajuan Shi ◽  
Jiaqi Yan ◽  
Changyou Zhang ◽  
Liping Liu
Keyword(s):  
Differential Privacy ◽  
Fog Computing ◽  
Privacy Preserving ◽  
Data Publishing

OPTDP: Towards Optimal Personalized Trajectory Differential Privacy for Trajectory Data Publishing

2021 ◽  
Author(s):  
Wenqing Cheng ◽  
Ruxue Wen ◽  
Haojun Huang ◽  
Wang Miao ◽  
Chen Wang
Keyword(s):  
Differential Privacy ◽  
Data Publishing ◽  
Trajectory Data

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.

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