Jacobian-Guided Anisotropic Noise Reshaping for Enhancing Representation Utility under Local Differential Privacy
arXiv:2605.16812v2 Announce Type: replace Abstract: While Local Differential Privacy (LDP) serves as a foundational primitive for distributed data collection, its stringent noise injection requirement often leads to severe degradation in data utility. This degradation stems from the task-agnostic nature of conventional LDP mechanisms, which inject noise uniformly across all dimensions regardless of their relative importance to the downstream objective. To address this issue, we propose a novel approach that mitigates noise in task-relevant subspaces of the data representation. Our method ident
The ongoing tension between data utility and privacy in AI development necessitates new approaches to leverage private data effectively while avoiding degradation.
This development addresses a fundamental trade-off in AI, allowing for more robust models trained on sensitive data without sacrificing performance, which is crucial for ethical and regulatory compliance.
The ability to selectively inject noise into data representations means that future AI systems can maintain high utility while adhering to stringent privacy standards like Local Differential Privacy.
- · AI developers
- · Healthcare sector
- · Financial services
- · Privacy-focused tech companies
- · Traditional LDP mechanisms
- · Data brokers relying on less private data
Improved performance and broader adoption of AI applications in privacy-sensitive domains.
Reduced regulatory hurdles for deploying AI solutions that handle personal or confidential information.
Enhanced public trust in AI systems due to stronger inherent privacy guarantees, leading to increased data sharing for beneficial applications.
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Read at arXiv cs.LG