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
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
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 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.
arXiv Machine Learning
Jul 7
Efficient privacy loss accounting for subsampling and random allocation
arXiv:2602. 17284v2 Announce Type: replace Abstract: We consider the privacy amplification properties of a sampling scheme in which a user's data isused in $k$ steps chosen randomly and uniformly from a sequence (or set) of $t$ steps.
arXiv AI
Aug 7
ABC: Numerical Data Collection under Local Differential Privacy without Prior Knowledge
arXiv:2608. 05737v1 Announce Type: cross Abstract: Local Differential Privacy (LDP) provides strong privacy guarantees for collecting numerical data.
arXiv Machine Learning
Jun 2
PE-means: Improved Differentially Private $k$-means Clustering through Private Evolution
arXiv:2606. 00342v1 Announce Type: new Abstract: We study the problem of differentially private (DP) $k$-means clustering in Euclidean space.