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Node Attributed Query Access Algorithm Based on Improved Personalized Differential Privacy Protection in Social Network

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Abstract

The existing differential privacy for social network graphs data published method is mainly focused on graph synthesis. But the privacy budget is set by data owner without adequate consideration of the differences in privacy requirements between individual users. And it published a topology data of social network data, which does not combine the independent attribute information and attribute information of the individual user with the correlation of edge information. This paper researches the influence of both social network attribute graph node properties and the correlation of edge information under the condition of considering the user privacy requirements, thus a social network attributes graphs algorithm is proposed under personalized differential privacy, which is for the independent attribute information between users. Since the node properties do not match the data type, our proposed mode will make the data partitioning in the data set, and it also divides and calculates the probability according to the node attribute distribution query function. Finally, through two real social network data sets, our proposed algorithms will execute for experimental comparison, and are verified they validity and usability through the experimental results.

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Acknowledgements

This work was financially supported by The National Natural Science Foundation of China under Grant No. 61404001 and The Top Talents Cultivation Project of Anhui Colleges and Universities under Grant No. gxbjZD15.

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Authors and Affiliations

  1. Anhui University of Science and Technology, Huainan, 232001, China

    Xiaobo Yin, Shunxiang Zhang & Hui Xu

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  1. Xiaobo Yin
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  2. Shunxiang Zhang
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  3. Hui Xu
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Correspondence to Xiaobo Yin.

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Yin, X., Zhang, S. & Xu, H. Node Attributed Query Access Algorithm Based on Improved Personalized Differential Privacy Protection in Social Network. Int J Wireless Inf Networks 26, 165–173 (2019). https://doi.org/10.1007/s10776-019-00441-y

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  • DOI: https://doi.org/10.1007/s10776-019-00441-y

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