arXiv Machine Learning
Jun 11
Fixed-Parameter Tractability of Private Synthetic Data Generation
arXiv:2606. 11283v1 Announce Type: cross Abstract: We study the problem of generating synthetic data under differential privacy.
Differentially Private Range Subgraph Counting
arXiv:2606. 08179v1 Announce Type: cross Abstract: Subgraph counting is a fundamental problem in graph analysis.
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arXiv Machine Learning
Aug 6
PriDyG: Privacy-preserving Dynamic Graph Inference with LLM-GNN Collaboration
arXiv:2608. 04255v1 Announce Type: cross Abstract: Graph inference over relational data can expose sensitive edge information, and this risk becomes more severe in dynamic graphs, where repeated model updates cause privacy loss to accumulate.
arXiv Machine Learning
5d ago
Differentially-Private Decision Trees and Provable Robustness to Data Poisoning
The paper introduces PrivaTree, a differentially‑private decision tree algorithm that uses private histograms to select splits while preserving a small privacy budget. PrivaTree supports mixed numerical and categorical data without leaking information about numerical features and achieves a superior privacy‑utility trade‑off compared to existing methods. Additionally, the authors provide theoretical bounds on the expected accuracy and success rates of backdoor attacks, showing that PrivaTree-trained trees are more robust against data poisoning than standard decision trees.
arXiv Machine Learning
Jul 10
EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy
arXiv:2607. 08659v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) have shown considerable success in learning from graph-structured data, but their use in privacy-sensitive areas remains difficult because graph structure can leak sensitive link information.
arXiv Machine Learning
Jul 30
Differentially Private Permutation Tests
arXiv:2310. 19043v3 Announce Type: replace-cross Abstract: Recent years have witnessed growing concerns about the privacy of sensitive data.
arXiv Machine Learning
Sep 1
Revisiting the Provable-Auditable Privacy Gap of DP-SGD
arXiv:2608.28934v1 Announce Type: new Abstract: Differential privacy (DP) has traditionally been used to provide theoretical upper bounds on an algorithm's stability to changing its training data. In...
arXiv Machine Learning
Jun 5
Minimax optimal differentially private synthetic data for smooth queries
arXiv:2602. 01607v3 Announce Type: replace-cross Abstract: Differentially private synthetic data enables the sharing and analysis of sensitive datasets while providing rigorous privacy guarantees for individual contributors.
arXiv Machine Learning
Jul 2
The Binary Tree Mechanism is Optimal for Approximate Differentially Private Continual Counting
arXiv:2607. 00876v1 Announce Type: cross Abstract: Private continual counting is a fundamental problem in differential privacy: given a binary stream of length $n$, where each $1$ corresponds to the contribution of one individual, the goal is to release all running counts while protecting the privacy of each individual.
arXiv Machine Learning
Jul 7
Towards Personalized Differentially Private Learning for Decentralized Local Graphs
arXiv:2607. 04777v1 Announce Type: new Abstract: Graph-structured data is increasingly generated and stored in decentralized environments, such as social platforms, mobile applications, and edge networks, where users maintain control over their local graph data.
arXiv Machine Learning
Jun 4
Bayesian Membership Privacy for Graph Neural Networks
arXiv:2606. 04069v1 Announce Type: cross Abstract: Existing privacy analyses for Graph Neural Networks (GNNs) largely inherit assumptions from non-graph settings, overlooking structural correlations and stochastic training-graph sampling.
arXiv Machine Learning
Sep 23
Empirical Auditing of Edge-Private Graph Generators
The paper presents an empirical audit of privacy leakage in edge‑private graph generators by testing whether outputs from edge‑neighbouring inputs remain distinguishable. It introduces statistically valid lower bounds on privacy loss and compares direct‑edge, local‑structural, and GNN‑based attacks based on the geometry around a target edge. Experiments on two generators and two networks reveal that privacy leakage varies with both the mechanism and the network, and that learned representations expose information beyond conventional local statistics.
arXiv AI
Jul 14
PromptGraph: Graph-Guided Prompt Sanitization for Balancing Privacy and Utility in LLM Inference
arXiv:2607. 10709v1 Announce Type: cross Abstract: Large Language Model (LLM) services introduce a fundamental privacy challenge.