tensorflow / tensorflow/privacy
Calculating the privacy budget in the absence of additive noise
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- Dominant language
- Python
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Description
Hello. The "rdp_accountant" code assumes an additive Sampled Gaussian Mechanism (SGM). However, I am interested in calculating the privacy budget (epsilon and delta) when no noise is added. In this case, the privacy is offered by other adjustments on the queries (gradients). Any idea on how to calculate the privacy measures in this case? Many thanks in advance.
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
Start by reading the rdp_accountant code and its assumptions about the additive Sampled Gaussian Mechanism. Clarify how the query or gradient adjustments provide privacy without additive noise, then define what calculation and validation would count as done for epsilon and delta.
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Assessment
- Tech stack
- python
- 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