tensorflow / tensorflow/privacy
Insecure Random Number Generator
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- Python
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Description
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
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
No repository file, test, or entry point is named. Start by reviewing the TensorFlow RNG documentation, the Opacus secure RNG API, and the linked differential-privacy research; done requires a project-level decision on secure random sampling and floating-point vulnerability protection.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, tensorflow
- Domain
- machine-learning, security
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100