English

Synthetic Data, Similarity-based Privacy Metrics, and Regulatory (Non-)Compliance

Cryptography and Security 2024-07-29 v2 Artificial Intelligence Computers and Society

Abstract

In this paper, we argue that similarity-based privacy metrics cannot ensure regulatory compliance of synthetic data. Our analysis and counter-examples show that they do not protect against singling out and linkability and, among other fundamental issues, completely ignore the motivated intruder test.

Keywords

Cite

@article{arxiv.2407.16929,
  title  = {Synthetic Data, Similarity-based Privacy Metrics, and Regulatory (Non-)Compliance},
  author = {Georgi Ganev},
  journal= {arXiv preprint arXiv:2407.16929},
  year   = {2024}
}

Comments

Accepted to the 2nd Workshop on Generative AI and Law (GenLaw 2024), part of ICML 2024