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- W3216519441 abstract "Differential privacy (DP) has become the de facto standard of privacy protection. For graphs, there are two widely used definitions of differential privacy, namely, edge differential privacy (edge-DP) and node differential privacy (node-DP), and node-DP is preferred when the minimal unit of interest is a node. To preserve node-DP, one can develop different methods to answer each specific graph query, or develop a graph publishing method to answer all graph queries. However, no existing works worked on such graph publishing methods. In this work, we propose two methods for publishing graphs under node-DP. One is the node-level perturbation algorithm which modifies the input graph by randomly inserting and removing nodes. The other one is the edge-level perturbation algorithm which randomly removing edges and inserting nodes. Both methods can achieve a flexible privacy guarantee by adjusting the running parameters. We conduct extensive experiments on both real-world and synthetic graphs to show the effectiveness and efficiency of proposed algorithms." @default.
- W3216519441 created "2021-12-06" @default.
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- W3216519441 date "2023-04-01" @default.
- W3216519441 modified "2023-10-02" @default.
- W3216519441 title "Publishing Graphs Under Node Differential Privacy" @default.
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- W3216519441 doi "https://doi.org/10.1109/tkde.2021.3128946" @default.
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