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Allowing users to use large target_epsilon
for debugging and research
#604
Comments
Hi @jeandut, if I understood correctly, the Could you provide privacy parameters ( Have you considered using the |
Thank you so much @Solosneros for your quick answer ! In my use case I am just tracing a DP curve for a research article and want to make sure that my implementation is asymptotically equivalent to the one wo DP by taking epsilon very large aka for debugging purposes.
Or
As for the reason I am using |
Note that indeed using RDP instead of PRV allows to use high epsilons. Feel free to either close or relabel my issue. |
Hi @jeandut, sorry for the delay. We tried out our fix further and while it seems a bit more robust it still fails occasionally. We'll get back to this but maybe somebody else has a fix. The RDP workaround definitely works. |
I posted a potential fix to this to the prv github but I am not sure if it is valid. Let's see what the folks from Microsoft say. microsoft/prv_accountant#36 (comment) |
PR is open #606. |
Thanks! Will take a look. |
Summary: ## Types of changes - [x] Bug fix (non-breaking change which fixes an issue) - [ ] New feature (non-breaking change which adds functionality) - [ ] Breaking change (fix or feature that would cause existing functionality to change) - [ ] Docs change / refactoring / dependency upgrade ## Motivation and Context / Related issue Hi, this PR fixes #601 and #604. It will introduce the same fix as in microsoft/prv_accountant#38. Lukas (author of prv accountant, wulu473) said that `In general, adding any additional points is safe and won't affect the robustness negatively.` The cause of these errors seems to be the grid for computing the `mean()` function of the `PrivacyRandomVariableTruncated` class. The grid (`points` variable) used to compute the mean is constant apart from the lowest (`self.t_min`) and highest point (`self.t_max`). This PR determines the grid (`points` variable) based on the lowest and highest point. More information is below. Best **Observation** I debugged the code and arrived at some point at the `mean()` function of the `PrivacyRandomVariableTruncated` class. The grid (`points` variable) used to compute the mean is constant apart from the lowest (`self.t_min`) and highest point (`self.t_max`). See the line of code [here](https://github.com/microsoft/prv_accountant/blob/a95c4e2d41ff4886c3e4a84925edf878a6540e0a/prv_accountant/privacy_random_variables/abstract_privacy_random_variable.py#L52). It looks like this `[self.tmin, -0.1, -0.01, -0.001, -0.0001, -1e-05, 1e-05, 0.0001, 0.001, 0.01, 0.1, self.tmax]`. It seems that the `tmin` and `tmax` are of the order of `[-12,12]` for the examples that I posted above and even up to `[-48,48]` for the example that jeandut posted in the #604 issue whereas they are more like `[-7,7]` for the [readme example for DP-SGD](https://github.com/microsoft/prv_accountant#dp-sgd). We suspect that the integration breaks down when the gridspacing between between `tmin` / `tmax` get's too large. **Proposed solution** Determine the points grid based on `tmin` and `tmax` but determines the start and end of the logspace based on `tmin` and `tmax`. Before: (https://github.com/pytorch/opacus/blob/95df0904ae5d2b3aaa26b708e5067e9271624036/opacus/accountants/analysis/prv/prvs.py#L99-L106) After: ``` # determine points based on t_min and t_max lower_exponent = int(np.log10(np.abs(self.t_min))) upper_exponent = int(np.log10(self.t_max)) points = np.concatenate( [ [self.t_min], -np.logspace(start=lower_exponent, stop=-5, num=10), [0], np.logspace(start=-5, stop=upper_exponent, num=10), [self.t_max], ] ) ``` ## How Has This Been Tested (if it applies) I ran the examples from the issues #601 and #604 and they don't break anymore. ``` import opacus target_delta = 0.001 target_epsilon = 20 steps = 5000 sample_rate=0.19120458891013384 for target_epsilon in [20, 50]: noise_multiplier = opacus.privacy_engine.get_noise_multiplier(target_delta=target_delta, target_epsilon=target_epsilon, steps=steps, sample_rate=sample_rate, accountant="prv") prv_accountant = opacus.accountants.utils.create_accountant("prv") prv_accountant.history = [(noise_multiplier, sample_rate, steps)] obtained_epsilon = prv_accountant.get_epsilon(delta=target_delta) print(f"target epsilon {target_epsilon}, obtained epsilon {obtained_epsilon}") ``` > target epsilon 20, obtained epsilon 19.999332284974717 target epsilon 50, obtained epsilon 49.99460075990896 ``` target_epsilon = 4 batch_size = 50 epochs = 5 delta = 1e-05 expected_len_dataloader = 500 // batch_size sample_rate = 1/expected_len_dataloader noise_multiplier = opacus.privacy_engine.get_noise_multiplier(target_delta=target_delta, target_epsilon=target_epsilon, epochs=epochs, sample_rate=sample_rate, accountant="prv") prv_accountant = opacus.accountants.utils.create_accountant("prv") prv_accountant.history = [(noise_multiplier, sample_rate, int(epochs / sample_rate))] obtained_epsilon = prv_accountant.get_epsilon(delta=target_delta) print(f"target epsilon {target_epsilon}, obtained epsilon {obtained_epsilon}") ``` > target epsilon 4, obtained epsilon 3.9968389923130356 ## Checklist - [x] The documentation is up-to-date with the changes I made. - [x] I have read the **CONTRIBUTING** document and completed the CLA (see **CONTRIBUTING**). - [ ] All tests passed, and additional code has been covered with new tests. Not able to run all tests locally and unsure if new tests should be added. Pull Request resolved: #606 Reviewed By: HuanyuZhang Differential Revision: D50111887 fbshipit-source-id: 2f77f8bc0e59837f765b87f2e107bc01015b9481
Committed PR #606 so I close this issue. |
🚀 Feature
Allowing users to use large
target_epsilon
when making their pipeline DP (as input toget_noise_multiplier
beforemake_private
)Motivation
Currently using a target budget of epsilon
> 10
. is generally not supported by Opacus for most datasets / batch sizes / number of gradient steps because of discretization issues stemming from Microsoft'sprv_accountant
(microsoft/prv_accountant#36) from which opacus'sPRVAccountant
implementation originates).I argue that this should be changed for multiple reasons:
epsilon=10
is completely arbitrary. It is well known that epsilon is highly application-dependent: for some applications it might makes sense perfectly to use epsilon even as high as 50 if other mitigations are in place or if participants agree to release their data under this privacy setting; for some other applicationsepsilon=10
could be considered way too high if the gradients are deemed extremely sensitive for instance (remember thatexp(10.)=22,026
).Warning the user that the privacy budget they set could be considered as high by some industries should be sufficient.
What do you think ?
In the meantime is there a relatively simple hack one could use in research experiments to use large target epsilons ?
Pitch
User provides a large
target_epsilon
say 50. inget_noise_multiplier
and it runs wo throwing:By instead displaying a warning.
Alternatives
In the meantime is there a relatively simple hack one could use in research experiments to use large target epsilons ? Such as exposing another custom
get_noise_multiplier
function wo this limitation ?Additional context
I was hesitating between posting it in prv_accountant or here
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