arXiv:2410.19371v3 Announce Type: replace-cross Abstract: Differential privacy (DP) provides robust privacy guarantees for statistical inference, but this can lead to unreliable results and biases in downstream applications. While several noise-aware approaches have been proposed which integrate DP perturbation into the inference, they are limited to specific types of simple probabilistic models. In this work, we propose a novel method for noise-aware approximate Bayesian inference based on stochastic gradient variational inference which can also be applied to high-dimensional and non-conjugat
The increasing focus on privacy in AI systems, especially with large language models and sensitive data, makes innovations in differential privacy highly relevant now.
This development offers a potential pathway to more robust and reliable privacy-preserving AI, crucial for applications involving sensitive personal or institutional data.
This research expands the applicability of noise-aware differential privacy to more complex and high-dimensional AI models, moving beyond previous limitations to simpler probabilistic models.
- · AI developers
- · Healthcare sector
- · Financial institutions
- · Privacy-focused technology companies
- · Malicious actors
- · Entities with weak data privacy practices
Increased adoption of differentially private AI models in sensitive applications due to enhanced reliability.
Improved public trust in AI systems that handle personal data, potentially accelerating AI integration into regulated industries.
A competitive advantage for organizations that can effectively implement advanced privacy-preserving AI, leading to new market standards for data security.
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Read at arXiv cs.LG