Paper 2022/360

Privacy-Preserving Contrastive Explanations with Local Foil Trees

Thijs Veugen, Bart Kamphorst, and Michiel Marcus

Abstract

We present the first algorithm that combines privacy-preserving technologies and state-of-the-art explainable AI to enable privacy-friendly explanations of black-box AI models. We provide a secure algorithm for contrastive explanations of black-box machine learning models that securely trains and uses local foil trees. Our work shows that the quality of these explanations can be upheld whilst ensuring the privacy of both the training data, and the model itself.

Note: To be published in CSCML 2022

Metadata
Available format(s)
PDF
Category
Applications
Publication info
Preprint. Minor revision.
Keywords
Secure multi-party computationexplainable AIdecision tree
Contact author(s)
thijs veugen @ tno nl
History
2022-03-30: last of 3 revisions
2022-03-18: received
See all versions
Short URL
https://ia.cr/2022/360
License
Creative Commons Attribution
CC BY

BibTeX

@misc{cryptoeprint:2022/360,
      author = {Thijs Veugen and Bart Kamphorst and Michiel Marcus},
      title = {Privacy-Preserving Contrastive Explanations with Local Foil Trees},
      howpublished = {Cryptology ePrint Archive, Paper 2022/360},
      year = {2022},
      note = {\url{https://eprint.iacr.org/2022/360}},
      url = {https://eprint.iacr.org/2022/360}
}
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