Correlated network data publication via differential privacy

2013 ◽  
Vol 23 (4) ◽  
pp. 653-676 ◽  
Author(s):  
Rui Chen ◽  
Benjamin C. M. Fung ◽  
Philip S. Yu ◽  
Bipin C. Desai
Keyword(s):  
Differential Privacy ◽  
Network Data ◽  
Data Publication

DIFFERENTIAL PRIVACY FOR ENERGY DATA PUBLICATION

2021 ◽  
Author(s):  
G. Agoua ◽  
P. Cauchois ◽  
O. Chaouy ◽  
I. Gazeau ◽  
B. Grossin
Keyword(s):  
Differential Privacy ◽  
Energy Data ◽  
Data Publication

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 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 DynaPro: Dynamic Wireless Sensor Network Data Protection Algorithm in IoT via Differential Privacy

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 167754-167765
Author(s):  
SongYan Li ◽  
Zhaobin Liu ◽  
Zhiyi Huang ◽  
Haoze Lyu ◽  
Zhiyang Li ◽  
...  
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Data Protection ◽  
Differential Privacy ◽  
Wireless Sensor ◽  
Network Data

scholarly journals Privacy Preserving Social Network Data Publication

2016 ◽  
Vol 18 (3) ◽  
pp. 1974-1997 ◽  
Author(s):  
Jemal H. Abawajy ◽  
Mohd Izuan Hafez Ninggal ◽  
Tutut Herawan
Keyword(s):  
Social Network ◽  
Privacy Preserving ◽  
Network Data ◽  
Data Publication ◽  
Social Network Data
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