arXiv Machine Learning
Understanding Private Evolution as Learning-Augmented Clustering
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Hugging Face Trending Papers
5d ago
Understanding Private Evolution as Learning-Augmented Clustering
Private Evolution (PE) is a differentially private algorithm for synthetic data generation. While it can be viewed as a Wasserstein learning algorithm, it performs much better in practice than worst-c...
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.
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
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
Jun 8
SecretFan: Synthesizing Realistic Data without Breaking Privacy
arXiv:2602. 05833v2 Announce Type: replace Abstract: There is a need for synthetic training and test datasets that replicate statistical distributions of original datasets without compromising their confidentiality.
arXiv Machine Learning
2d ago
Certification-Based Differentially Private Learning
arXiv:2609.39629v1 Announce Type: new Abstract: Differential privacy (DP) in machine learning is typically achieved by adding noise to model parameters (private learning) or to model outputs (private...