arXiv:2602.15919v2 Announce Type: replace-cross Abstract: Recent work in the privacy literature shows that sample-targeted membership inference attacks (MIAs) significantly outperform untargeted approaches by a wide margin. Motivated by this observation, we address the following question: can the privacy vulnerability of individual training points be assessed without training shadow models? We show that per-sample exposure to MIA is governed not only by a point's loss, but also by a data-dependent geometric measure. In the linear setting, we derive a closed-form decomposition of individual bla
The proliferation of AI systems and the increasing focus on data privacy necessitate robust methods for assessing and mitigating privacy risks, leading to a surge in research in this area.
This research provides a more efficient method to evaluate the privacy vulnerabilities of individual training data points, which is crucial for developing and deploying privacy-preserving AI systems without extensive and costly retraining.
The ability to assess per-sample membership inference vulnerability without retraining simplifies the development and validation of privacy-preserving machine learning models, making it easier to identify and protect sensitive data.
- · AI developers
- · Privacy researchers
- · Organizations handling sensitive data
- · Data subjects
- · Malicious actors attempting MIAs
- · Organizations with lax data privacy practices
Easier and faster assessment of data privacy risks in AI models, reducing development cycles and costs.
Improved adoption of privacy-preserving machine learning techniques due to lower implementation barriers.
Increased public trust in AI systems handling personal data, potentially accelerating AI integration into sensitive sectors.
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Read at arXiv cs.LG