https://github.com/google-deepmind/informed_adversary_mnist_reconstruction

https://github.com/google-deepmind/informed_adversary_mnist_reconstruction

Science Score: 23.0%

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    Found 2 DOI reference(s) in README
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    Links to: arxiv.org
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    Low similarity (7.7%) to scientific vocabulary
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Repository

Basic Info
  • Host: GitHub
  • Owner: google-deepmind
  • License: apache-2.0
  • Language: Jupyter Notebook
  • Default Branch: main
  • Size: 21.5 KB
Statistics
  • Stars: 16
  • Watchers: 5
  • Forks: 1
  • Open Issues: 0
  • Releases: 0
Archived
Created almost 4 years ago · Last pushed almost 4 years ago
Metadata Files
Readme Contributing License

README.md

informedadversarymnist_reconstruction

This is a minimal implementation of a training data reconstruction attack with an informed adversary on MNIST, as described in Balle et al. (2021).

Usage

Usage instructions are included in the Colabs which open and run on the free-to-use Google Colab platform - just click the buttons below! Improved performance and longer timeouts are available with Colab Pro.

informedadversarymnistreconstruction [Open In Colab](https://colab.research.google.com/github/deepmind/informedadversarymnistreconstruction/blob/master/informedadversarymnist_reconstruction.ipynb)

Citing this work

If you use this code (or any derived code), please cite the relevant accompanying paper.

@INPROCEEDINGS {, author = {B. Balle and G. Cherubin and J. Hayes}, booktitle = {2022 2022 IEEE Symposium on Security and Privacy (SP) (SP)}, title = {Reconstructing Training Data with Informed Adversaries}, year = {2022}, volume = {}, issn = {2375-1207}, pages = {1556-1556}, keywords = {machine-learning,-neural-networks,-reconstruction-attacks,-differential-privacy}, doi = {10.1109/SP46214.2022.00127}, url = {https://doi.ieeecomputersociety.org/10.1109/SP46214.2022.00127}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, month = {may} }

Disclaimer

This is not an official Google product.

Owner

  • Name: Google DeepMind
  • Login: google-deepmind
  • Kind: organization

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