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PPDU: dynamic graph publication with local differential privacy

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Abstract

Local differential privacy (LDP) is an emerging privacy-preserving data collection model that requires no trusted third party. Most privacy-preserving decentralized graph publishing studies adopt LDP technique to ensure individual privacy. However, existing LDP-based synthetic graph generation approaches focus on static graph publishing and can only republish synthetic graphs in a brute-force manner when dealing with dynamic graph problems, resulting in low synthetic graph accuracy. The main difficulties come from the two steps of dynamic graph publishing: excessive noise injection in initial graph generation and over-segmentation of the privacy budget in graph update. We address these two issues by presenting PPDU, the first dynamic graph publication approach under LDP. PPDU uses a privacy-preference-specifying mechanism to untie the noise injection and the graph size, significantly reducing noise injection. We then divide the privacy-preserving graph update problem into three subproblems: node insertion, edge insertion, and edge deletion, and propose update threshold-based dynamic graph releasing methods to avoid excessive segmentation of the privacy budget, thereby significantly improving the accuracy of synthetic graphs. Theoretical analysis and experimental results prove that our solution can continually yield high-quality dynamic graphs while satisfying edge LDP.

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Notes

  1. In this paper, we use ’node’ or ’user’ according to the context without distinction, the same goes for ’network’ and ’graph’.

  2. For node deletion, we simply delete the node and all edges incident to it, and we give the reason in Sect. 4.

  3. Self-loop edges will be removed, and DDGU will sample new edges to replace them.

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Acknowledgements

The work was supported by the National Natural Science Foundation of China under grant 61772131. Our source code is available at a GitHub repository (https://github.com/will-1996/PPDU). We also thank the reviewers for their kind and helpful comments.

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Author notes

  1. Sen Zhang, Nan Fu and Dongyue Zhang contributed equally to this work.

Authors and Affiliations

  1. School of Computer Science and Engineering, Southeast University, Nanjing, 211189, Jiangsu, China

    Lihe Hou, Weiwei Ni, Sen Zhang, Nan Fu & Dongyue Zhang

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  1. Lihe Hou
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  2. Weiwei Ni
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  3. Sen Zhang
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Correspondence to Lihe Hou.

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Hou, L., Ni, W., Zhang, S. et al. PPDU: dynamic graph publication with local differential privacy. Knowl Inf Syst 65, 2965–2989 (2023). https://doi.org/10.1007/s10115-023-01838-1

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