Informationally Compressive Anonymization: Non-Degrading Sensitive Input Protection for Privacy-Preserving Supervised Machine Learning
arXiv:2603.15842v2 Announce Type: replace Abstract: Modern machine learning systems increasingly rely on sensitive data, creating significant privacy, security, and regulatory risks that existing privacy-preserving machine learning (ppML) techniques, such as Differential Privacy (DP) and Homomorphic Encryption (HE), address only at the cost of degraded performance, increased complexity, or prohibitive computational overhead. This paper introduces Informationally Compressive Anonymization (ICA) and the VEIL architecture, a privacy-preserving ML framework that achieves strong privacy guarantees
The increasing reliance on sensitive data in modern machine learning systems and the corresponding regulatory pressures are driving immediate demand for more effective privacy-preserving solutions.
This development represents a potential breakthrough in privacy-preserving machine learning, offering a technique that promises stronger privacy guarantees without the significant performance degradation or computational overhead associated with existing methods.
The introduction of Informationally Compressive Anonymization (ICA) and the VEIL architecture could enable broader and more secure adoption of AI in sensitive domains, potentially shifting the trade-offs between privacy and utility.
- · Healthcare sector
- · Financial institutions
- · Cloud AI providers
- · AI developers
- · Data privacy violators
- · Legacy ppML techniques (if ICA proves superior)
Increased adoption of privacy-preserving machine learning across industries handling sensitive data.
Reduced litigation risks and regulatory compliance costs for AI-driven businesses handling personal information.
Accelerated development and deployment of AI applications in highly regulated markets, fostering new economic growth in data-sensitive sectors.
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Read at arXiv cs.LG