arXiv Machine Learning
Jun 9
Differentially Private Range Subgraph Counting
arXiv:2606. 08179v1 Announce Type: cross Abstract: Subgraph counting is a fundamental problem in graph analysis.
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.
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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 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 Machine Learning
Aug 12
Information Bottleneck under Perfect Privacy
arXiv:2608. 11003v1 Announce Type: cross Abstract: In this work, we study the information bottleneck under perfect privacy, with particular emphasis on the active-rate regime, where the representation-rate constraint is binding and directly limits the achievable utility.
arXiv AI
Jul 14
Private Seeds, Public LLMs: Realistic and Privacy-Preserving Synthetic Data Generation
arXiv:2604. 07486v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have emerged as a powerful tool for synthetic data generation.
Hugging Face Trending Papers
Jun 11
Disparate Impact in Synthetic Data Generation
We revisit the fairness notion of disparate impact for synthetic data generation (SDG), that assesses whether the utility of generated records is the same across sensitive groups. Our approach departs from existing work on fair SDG, that address the problem of correcting for undue biases in the observed distribution, hence redefining SDG as learning a distribution that is not that of the real data.
arXiv Machine Learning
Aug 4
Private Generative Bootstrap via Blocking
arXiv:2608. 02480v1 Announce Type: cross Abstract: With AI systems gaining more access to individuals' information, it is important to protect privacy when reporting statistical answers.
Hugging Face Trending Papers
Aug 3
Private Generative Bootstrap via Blocking
With AI systems gaining more access to individuals' information, it is important to protect privacy when reporting statistical answers. Equally important is to privatize the reporting of uncertainty in such answers.
arXiv Machine Learning
Jun 9
Differentially Private Synthetic Data via APIs 4: Tabular Data
arXiv:2606. 08259v1 Announce Type: new Abstract: This paper investigates the problem of generating synthetic tabular data with differential privacy (DP) guarantees, enabling data sharing in sensitive domains.
arXiv Machine Learning
Jul 1
Private Rate-Constrained Optimization with Applications to Fair Learning
arXiv:2505. 22703v2 Announce Type: replace Abstract: Many problems in trustworthy ML can be expressed as constraints on prediction rates across subpopulations, including group fairness constraints (demographic parity, equalized odds, etc.
arXiv Machine Learning
Jul 9
A Van Trees Lower Bound for Fully Interactive Differentially Private Federated Learning
arXiv:2605. 19813v2 Announce Type: replace Abstract: Federated differentially private protocols can communicate over many adaptive rounds and reuse each client's local samples.
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.