ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2016, Vol. 53 ›› Issue (5): 1106-1117.doi: 10.7544/issn1000-1239.2016.20150304

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Accurate Histogram Release under Differential Privacy

Zhang Xiaojian1, Shao Chao1, Meng Xiaofeng2   

  1. 1(College of Computer & Information Engineering, Henan University of Economics and Law, Zhengzhou 450002); 2(School of Information, Renmin University of China, Beijing 100872)
  • Online:2016-05-01

Abstract: Grouping-based differentially private histogram release has attracted considerable research attention in recent years. The trade-off between approximation error caused by the group’s mean and Laplace error due to Laplace noise constrains the accuracy of histogram release. Most existing methods based on grouping strategy cannot efficiently accommodate the both errors. This paper proposes an efficient differentially private method, called DiffHR (differentially private histogram release) to publish histograms. In order to boost the accuracy of the released histogram, DiffHR employs Metropolis-Hastings method in MCMC (Markov chain Monte Carlo) and the exponential mechanism to propose an efficient sorting method. This method generates a differentially private histogram by sampling and exchanging two buckets to approximate the correct order. To balance Laplace error and approximation error efficiently, a utility-driven adaptive clustering method is proposed in DiffHR to partition the sorted histogram. Furthermore, the time complexity of the clustering method is O(n). DiffHR is compared with existing methods such as GS, AHP on the real datasets. The experimental results show that DiffHR outperforms its competitors, and achieves the accurate results.

Key words: differential privacy, histogram release, grouping, Laplace error, approximation error

CLC Number: