A Differentially Private Weighted Empirical Risk Minimization Procedure and its Application to Outcome Weighted Learning
arXiv:2307.13127v3 Announce Type: replace-cross Abstract: Data used to train predictive models via empirical risk minimization (ERM) often contain sensitive personal information. While differential privacy (DP) provides mathematically provable bounds to protect such data, previous work has focused almost exclusively on unweighted ERM. We consider weighted ERM (wERM) -- an important generalization where individual contributions to the objective function vary. We propose the first DP algorithm for general wERM with formal privacy guarantees and derive both its empirical and population excess ris
Growing concerns about data privacy in AI models and the increasing complexity of real-world datasets necessitate more robust, nuanced privacy-preserving techniques like those for weighted empirical risk minimization.
This development offers a foundational method for building more ethical and privacy-compliant AI systems, particularly relevant for sensitive applications like healthcare and finance where data confidentiality is paramount.
The ability to apply differential privacy to weighted empirical risk minimization opens new avenues for AI development in scenarios where data points have varying importance, without compromising individual privacy.
- · Healthcare sector
- · Financial institutions
- · Privacy-focused AI developers
- · Data subjects
- · AI models lacking strong privacy guarantees
- · Organizations with lax data governance
More widespread adoption of differentially private machine learning in sensitive industries.
Increased trust in AI applications that handle personal data, potentially accelerating AI integration into privacy-critical domains.
Development of new regulatory standards and compliance requirements based on advanced differential privacy techniques.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG