arXiv:2601.07545v2 Announce Type: replace Abstract: We study differentially private ordinary least squares (DP-OLS) with bounded data $(X,Y)$ via sketching-based mechanisms. While Gaussian sketching approaches have been explored for DP-OLS \citep{sheffet2017differentially}, they are typically viewed as less competitive than the Adaptive Sufficient Statistics Perturbation (AdaSSP) method \citep{wang_adassp}, which directly perturbs the sufficient statistics $(X^{\top}X, X^{\top}Y)$. This method was shown to be close to information-theoretically optimal, while also exhibiting strong empirical pe
This research addresses a critical need in AI development for private and secure data handling, especially as regulatory pressures for data privacy increase and AI models leverage increasingly sensitive information.
Improved differential privacy techniques for linear regression allow for more reliable and secure AI models that can operate on sensitive data without compromising individual privacy, fostering trust and wider adoption.
The development of near-optimal private linear regression methods enhances the accuracy and efficiency of privacy-preserving machine learning, reducing the trade-off between privacy and model utility.
- · AI developers
- · Healthcare sector
- · Financial services
- · Data privacy advocates
- · Malicious actors seeking personal data
- · Organizations with poor data privacy practices
More secure and ethical deployment of machine learning models in sensitive domains.
Increased public and regulatory confidence in AI systems leading to broader adoption across industries handling personal data.
The establishment of new industry best practices and standards for data privacy in AI development, potentially leading to 'privacy-by-design' becoming a core AI development principle.
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