Abstract
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.
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Acknowledgements
This work was supported in part by the National Natural Science Foundation of China (41671443), Applied Basic Research Program of Wuhan (2016010101010024), the Open Funding of NUIST, PAPD, CICAEET and Startup Foundation for Introducing Talent of Nanjing University of Information Science and Technology under Grant 2016r055, the Fundamental Research Funds for the Central Universities (2042017kf0044), China Postdoctoral Science Foundation (Grant No. 2017M612511) and LIESMARS Special Research Funding. The authors are grateful for the anonymous reviewers who made constructive comments and improvements.
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Wang, H., Xu, Z., Xiong, L., Wang, T. (2017). Conducting Correlated Laplace Mechanism for Differential Privacy. In: Sun, X., Chao, HC., You, X., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2017. Lecture Notes in Computer Science(), vol 10603. Springer, Cham. https://doi.org/10.1007/978-3-319-68542-7_7
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DOI: https://doi.org/10.1007/978-3-319-68542-7_7
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