Hugging Face Trending Papers
Understanding Private Evolution as Learning-Augmented Clustering
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arXiv Machine Learning
4d ago
Understanding Private Evolution as Learning-Augmented Clustering
arXiv:2609.36678v1 Announce Type: new Abstract: Private Evolution (PE) is a differentially private algorithm for synthetic data generation. While it can be viewed as a Wasserstein learning algorithm,...
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
Jun 4
DPDL: Towards Differential Privacy Preservation in Decentralized Stochastic Learning on Non-IID Data
arXiv:2606. 04399v1 Announce Type: new Abstract: In the paradigm of decentralized learning, a group of agents collaborate to train a global model using distributed datasets without a central server.