Abstract
Under the premise to protect the privacy of personal information, publishing valuable graph is a challenging issue in privacy research. Appling differential privacy in graph, most of the work focused on graph structure characteristic values, because the basic of differential privacy is data distortion, it’s hard to get valuable composite graph if we add a large number of random noise into the raw data. In this article, we show the key that influence availability is whether the important data keep original value in a composite graph. We analysis the properties of important data of k triangle count, and provide a new method for synthesis graph publication. We show the application of this method in k triangle count, and the experimental results proved the accuracy of the method.
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Acknowledgments
This article is partly supported by the National Natural Science Foundation of China under Grant No. 61370084, and the China Numerical Tank Project.
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Sun, Y., Zhao, H., Han, Q., Li, L. (2017). Composite Graph Publication Considering Important Data. In: Zou, B., Li, M., Wang, H., Song, X., Xie, W., Lu, Z. (eds) Data Science. ICPCSEE 2017. Communications in Computer and Information Science, vol 727. Springer, Singapore. https://doi.org/10.1007/978-981-10-6385-5_18
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DOI: https://doi.org/10.1007/978-981-10-6385-5_18
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