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[2602.22282] Differentially Private Truncation of Unbounded Data via Public Second Moments
Summary
This paper presents a novel approach to differentially private data truncation using public second moments, enhancing privacy without compromising data utility.
Why It Matters
As data privacy concerns grow, especially in AI applications, this research addresses a critical limitation of differential privacy by enabling its application to unbounded data distributions. The proposed method could significantly improve the accuracy and stability of privacy-preserving models, making it relevant for researchers and practitioners in data science and AI.
Key Takeaways
- Introduces Public-moment-guided Truncation (PMT) for better data privacy.
- Demonstrates improved accuracy and stability in differentially private models.
- Establishes theoretical error bounds and robustness guarantees.
- Utilizes public second-moment information to enhance model performance.
- Applicable to various regression models, ensuring practical relevance.
Computer Science > Cryptography and Security arXiv:2602.22282 (cs) [Submitted on 25 Feb 2026] Title:Differentially Private Truncation of Unbounded Data via Public Second Moments Authors:Zilong Cao, Xuan Bi, Hai Zhang View a PDF of the paper titled Differentially Private Truncation of Unbounded Data via Public Second Moments, by Zilong Cao and 1 other authors View PDF HTML (experimental) Abstract:Data privacy is important in the AI era, and differential privacy (DP) is one of the golden solutions. However, DP is typically applicable only if data have a bounded underlying distribution. We address this limitation by leveraging second-moment information from a small amount of public data. We propose Public-moment-guided Truncation (PMT), which transforms private data using the public second-moment matrix and applies a principled truncation whose radius depends only on non-private quantities: data dimension and sample size. This transformation yields a well-conditioned second-moment matrix, enabling its inversion with a significantly strengthened ability to resist the DP noise. Furthermore, we demonstrate the applicability of PMT by using penalized and generalized linear regressions. Specifically, we design new loss functions and algorithms, ensuring that solutions in the transformed space can be mapped back to the original domain. We have established improvements in the models' DP estimation through theoretical error bounds, robustness guarantees, and convergence results, attr...