Tag: dp
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Differentially Private Decentralized Dataset Synthesis Through Randomized Mixing with Correlated Noise
Differentially Private Decentralized Dataset Synthesis Through Randomized Mixing with Correlated Noise arXiv:2509.10385v1 Announce Type: new Abstract: In this work, we explore differentially private synthetic data generation in a decentralized-data setting by building on the recently proposed Differentially Private Class-Centric Data Aggregation (DP-CDA). DP-CDA synthesizes data in a centralized setting by mixing multiple randomly-selected samples from…
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An Iterative Algorithm for Differentially Private $k$-PCA with Adaptive Noise
An Iterative Algorithm for Differentially Private $k$-PCA with Adaptive Noise arXiv:2508.10879v1 Announce Type: new Abstract: Given $n$ i.i.d. random matrices $A_i in mathbb{R}^{d times d}$ that share a common expectation $Sigma$, the objective of Differentially Private Stochastic PCA is to identify a subspace of dimension $k$ that captures the largest variance directions of $Sigma$, while…
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Optimal Regret of Bernoulli Bandits under Global Differential Privacy
Optimal Regret of Bernoulli Bandits under Global Differential Privacy arXiv:2505.05613v1 Announce Type: new Abstract: As sequential learning algorithms are increasingly applied to real life, ensuring data privacy while maintaining their utilities emerges as a timely question. In this context, regret minimisation in stochastic bandits under $epsilon$-global Differential Privacy (DP) has been widely studied. Unlike bandits…