Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank

• URL: http://arxiv.org/abs/2207.06944v3
• Date: Wed, 14 Feb 2024 19:58:13 GMT
• Title: Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank
• Authors: Alessandro Epasto, Vahab Mirrokni, Bryan Perozzi, Anton Tsitsulin, Peilin Zhong
• Abstract summary: We propose an algorithm which outputs an approximate PPR and has provably bounded sensitivity to input edges. Our sensitivity-bounded PPR directly implies private algorithms for several tools of graph learning, such as, differentially private (DP) PPR ranking, DP node classification, and DP node embedding.
• Score: 74.53857412207034
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Personalized PageRank (PPR) is a fundamental tool in unsupervised learning of graph representations such as node ranking, labeling, and graph embedding. However, while data privacy is one of the most important recent concerns, existing PPR algorithms are not designed to protect user privacy. PPR is highly sensitive to the input graph edges: the difference of only one edge may cause a big change in the PPR vector, potentially leaking private user data. In this work, we propose an algorithm which outputs an approximate PPR and has provably bounded sensitivity to input edges. In addition, we prove that our algorithm achieves similar accuracy to non-private algorithms when the input graph has large degrees. Our sensitivity-bounded PPR directly implies private algorithms for several tools of graph learning, such as, differentially private (DP) PPR ranking, DP node classification, and DP node embedding. To complement our theoretical analysis, we also empirically verify the practical performances of our algorithms.

Related papers

• CURATE: Scaling-up Differentially Private Causal Graph Discovery [8.471466670802817]
  Differential Privacy (DP) has been adopted to ensure user privacy in Causal Graph Discovery (CGD) We present CURATE, a DP-CGD framework with adaptive privacy budgeting. We show that CURATE achieves higher utility compared to existing DP-CGD algorithms with less privacy-leakage.
  arXiv  Detail & Related papers  (2024-09-27T18:00:38Z)
• Privacy-Preserving Graph Embedding based on Local Differential Privacy [26.164722283887333]
  We introduce a novel privacy-preserving graph embedding framework, named PrivGE, to protect node data privacy. Specifically, we propose an LDP mechanism to obfuscate node data and utilize personalized PageRank as the proximity measure to learn node representations. Experiments on several real-world graph datasets demonstrate that PrivGE achieves an optimal balance between privacy and utility.
  arXiv  Detail & Related papers  (2023-10-17T08:06:08Z)
• Independent Distribution Regularization for Private Graph Embedding [55.24441467292359]
  Graph embeddings are susceptible to attribute inference attacks, which allow attackers to infer private node attributes from the learned graph embeddings. To address these concerns, privacy-preserving graph embedding methods have emerged. We propose a novel approach called Private Variational Graph AutoEncoders (PVGAE) with the aid of independent distribution penalty as a regularization term.
  arXiv  Detail & Related papers  (2023-08-16T13:32:43Z)
• Privacy-Preserved Neural Graph Similarity Learning [99.78599103903777]
  We propose a novel Privacy-Preserving neural Graph Matching network model, named PPGM, for graph similarity learning. To prevent reconstruction attacks, the proposed model does not communicate node-level representations between devices. To alleviate the attacks to graph properties, the obfuscated features that contain information from both vectors are communicated.
  arXiv  Detail & Related papers  (2022-10-21T04:38:25Z)
• Individual Privacy Accounting for Differentially Private Stochastic Gradient Descent [69.14164921515949]
  We characterize privacy guarantees for individual examples when releasing models trained by DP-SGD. We find that most examples enjoy stronger privacy guarantees than the worst-case bound. This implies groups that are underserved in terms of model utility simultaneously experience weaker privacy guarantees.
  arXiv  Detail & Related papers  (2022-06-06T13:49:37Z)
• Degree-Preserving Randomized Response for Graph Neural Networks under Local Differential Privacy [8.12606646175019]
  We propose a novel LDP algorithm called the DPRR (Degree-Preserving Randomized Response) to provide LDP for edges in GNNs. Our DPRR preserves each user's degree hence a graph structure while providing edge LDP. We focus on graph classification as a task of GNNs and evaluate the DPRR using three social graph datasets.
  arXiv  Detail & Related papers  (2022-02-21T13:35:03Z)
• Partial sensitivity analysis in differential privacy [58.730520380312676]
  We investigate the impact of each input feature on the individual's privacy loss. We experimentally evaluate our approach on queries over private databases. We also explore our findings in the context of neural network training on synthetic data.
  arXiv  Detail & Related papers  (2021-09-22T08:29:16Z)
• Scaling Graph Neural Networks with Approximate PageRank [64.92311737049054]
  We present the PPRGo model which utilizes an efficient approximation of information diffusion in GNNs. In addition to being faster, PPRGo is inherently scalable, and can be trivially parallelized for large datasets like those found in industry settings. We show that training PPRGo and predicting labels for all nodes in this graph takes under 2 minutes on a single machine, far outpacing other baselines on the same graph.
  arXiv  Detail & Related papers  (2020-07-03T09:30:07Z)
• Locally Private Graph Neural Networks [12.473486843211573]
  We study the problem of node data privacy, where graph nodes have potentially sensitive data that is kept private. We develop a privacy-preserving, architecture-agnostic GNN learning algorithm with formal privacy guarantees. Experiments conducted over real-world datasets demonstrate that our method can maintain a satisfying level of accuracy with low privacy loss.
  arXiv  Detail & Related papers  (2020-06-09T22:36:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.