Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Privacy Considerations refresh #271

Merged
merged 2 commits into from
Jun 30, 2022
Merged

Privacy Considerations refresh #271

Changes from 1 commit
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
7d85c93
Privacy Considerations refresh
anssiko Jun 7, 2022
5185b9f
Clarify 'device type' is a normative type
anssiko Jun 16, 2022
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
6 changes: 6 additions & 0 deletions index.bs
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -436,6 +436,12 @@ Note: The group is <a href="https://github.com/webmachinelearning/webnn/issues/8

Unlike APIs like WebGL, and WebGPU; this API does not intrinsically support custom shader authoring; and as a result is not prone to timing attacks that rely on shader caches, or other persistent data. The API builds upon pre-existing shaders and lower level primitives of the browser or the underlying OS. Web developers who interface with {{GPUDevice}} are expected to be aware of <a href="https://gpuweb.github.io/gpuweb/#privacy-user-agent-state">WebGPU compilation cache considerations</a>.

The WebGPU API identifies <a href="https://gpuweb.github.io/gpuweb/#privacy-machine-artifacts">machine-specific artifacts</a> as a privacy consideration. Given the WebNN API defines means to record an ML worload onto a WebGPU-compatible {{GPUCommandBuffer}}, compute unit scheduling may under certain circumstances introduce a fingerprint. However, similarly to WebGPU, such fingerprints are identical across most or all of the devices of each vendor, mitigating the concern. Furthermore, software implementations can be used to further eliminate such artifacts.

The WebNN API defines two developer-settable preferences to help inform [[#programming-model-device-selection]] and allow the implementation better select the most appropriate underlying execution device for the workload. [=Device type=] preference indicates the kind of device and is either "cpu" or "gpu". If this preference cannot be satisfied, an {{OperationError}} exception is thrown, thus this preference can in some cases add two bits of entropy to the fingerprint. [=Power preference=] indicates preference as related to the power consumption and is considered a hint only and as such does not increase entropy of the fingerprint.
huningxin marked this conversation as resolved.
Show resolved Hide resolved

If a future version of this specification introduces support for new a [=device type=] that can only support a subset of {{MLOperandType}}s, that may introduce a new fingerprint.

In general, implementers of this API are expected to be familiar with the <a href="https://gpuweb.github.io/gpuweb/#privacy-considerations">WebGPU Privacy Considerations</a>.

Ethical Considerations {#ethics}
Expand Down