impressive work jmo - thanks for open sourcing this (and OSI-compliant)
we are working on a challenge which is somewhat like a homomorphic encryption problem - I'm wondering if OpenPCC could help in some way? :
When developing websites/apps, developers generally use logs to debug production issues. However with wearables, logs can be privacy issue: imagine some AR glasses logging visual data (like someone's face). Would OpenPCC help to extract/clean/anonymize this sort of data for developers to help with their debugging?
Reading the whitepaper, the inference provider still has the ability to access the prompt and response plaintext. This scheme does seem to guarantee that plaintext cannot be read for all other parties (e.g. the API router), and that the client's identity is hidden and cannot be associated with their request. Perhaps the precise privacy guarantees and allowances should be summarized in the readme.
With that in mind, does this scheme offer any advantage over the much simpler setup of a user sending an inference request:
- directly to an inference provider (no API router middleman)
- that accepts anonymous crypto payments (I believe such things exist)
That's nice... in theory. Like it could be cool, and useful... but like what would I actually run on it if I'm not a spammer?
Edit : reminds me of federated learning and FlowerLLM (training only AFAIR, not inference), like... yes, nice, I ALWAYS applaud any way to disentangle from proprieaty software and wall gardens... but like what for? What actual usage?
@dang can we modify the title to acknowledge that it's specific to chatbots? The title reads like this is about generic compute, and the content is emphatically not about generic compute.
I realize this is just bad branding by apple but it's still hella confusing.
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[ 3.4 ms ] story [ 31.0 ms ] threadwe are working on a challenge which is somewhat like a homomorphic encryption problem - I'm wondering if OpenPCC could help in some way? :
When developing websites/apps, developers generally use logs to debug production issues. However with wearables, logs can be privacy issue: imagine some AR glasses logging visual data (like someone's face). Would OpenPCC help to extract/clean/anonymize this sort of data for developers to help with their debugging?
With that in mind, does this scheme offer any advantage over the much simpler setup of a user sending an inference request:
- directly to an inference provider (no API router middleman)
- that accepts anonymous crypto payments (I believe such things exist)
- using a VPN to mask their IP?
Service: https://www.privatemode.ai/ Code: https://github.com/edgelesssys/privatemode-public
https://news.ycombinator.com/item?id=45746753
Edit : reminds me of federated learning and FlowerLLM (training only AFAIR, not inference), like... yes, nice, I ALWAYS applaud any way to disentangle from proprieaty software and wall gardens... but like what for? What actual usage?
I realize this is just bad branding by apple but it's still hella confusing.
[1] https://techcommunity.microsoft.com/blog/azureconfidentialco...