kairouzp
commented
2 years ago Thanks so much for pointing this out. We are aware of this issue (see Section 8 of https://arxiv.org/pdf/1812.06210.pdf).
On Mon, Nov 7, 2022 at 2:50 PM Gonzalo Munilla Garrido < @.***> wrote:
Hello,
I would like to bring to your attention that using the random number generator from TensorFlow could lead to vulnerabilities when sampling from a distribution to fulfill differential privacy during training: https://www.tmlt.io/research/tiny-bits-matter-precision-based-attacks-on-differential-privacy
PyTorch Opacus uses a secure RNG: https://opacus.ai/api/privacy_engine.html
In contrast, TensorFlow RNG: https://www.tensorflow.org/api_docs/python/tf/random/Generator
https://stackoverflow.com/questions/63350248/is-tf-random-normal-cryptographically-secure
Kind regards, Gonzalo
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Hello,
I would like to bring to your attention that using the random number generator from TensorFlow could lead to vulnerabilities when sampling from a distribution to fulfill differential privacy during training: https://www.tmlt.io/research/tiny-bits-matter-precision-based-attacks-on-differential-privacy
PyTorch Opacus uses a secure RNG: https://opacus.ai/api/privacy_engine.html
In contrast, TensorFlow RNG: https://www.tensorflow.org/api_docs/python/tf/random/Generator https://stackoverflow.com/questions/63350248/is-tf-random-normal-cryptographically-secure
Additionally, there is no documentation that states the use of floating-point vulnerability protection as in https://scholar.google.com/citations?view_op=view_citation&hl=en&user=hg3A9TgAAAAJ&citation_for_view=hg3A9TgAAAAJ:dhFuZR0502QC and https://research.ibm.com/publications/secure-random-sampling-in-differential-privacy
Kind regards, Gonzalo