Conducting Correlated Laplace Mechanism for Differential Privacy

Recently, differential privacy achieves good trade-offs between data publishing and sensitive information hiding. But in data publishing for correlated data, the independent Laplace noise implemented in current differential privacy preserving methods can be detected and sanitized, reducing privacy l...

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Bibliographic Details
Published inCloud Computing and Security pp. 72 - 85
Main Authors Wang, Hao, Xu, Zhengquan, Xiong, Lizhi, Wang, Tao
Format Book Chapter
LanguageEnglish
Published Cham Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
Correlated data
Correlated Laplace noise
Data publishing
Differential privacy
Privacy preserving
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Summary:Recently, differential privacy achieves good trade-offs between data publishing and sensitive information hiding. But in data publishing for correlated data, the independent Laplace noise implemented in current differential privacy preserving methods can be detected and sanitized, reducing privacy level. In prior work, we have proposed a correlated Laplace mechanism (CLM) to remedy this problem. But the concrete steps and detailed parameters to imply CLM and the complete proof has not been discussed. In this paper, we provide the complete proof and specific steps to conduct CLM. Also, we have verified the error of our implement method. Experimental results show that our method can retain small error to generate correlated Laplace noise for large quantities of queries.
ISBN:9783319685410
3319685414
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-319-68542-7_7