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OpenAI Privacy Filter

openai.com

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Re: OpenAI Privacy Filter

#51

Post by blfr »

fzxu22 wrote: post #37

Working on this: https://github.com/KevinXuxuxu/anon_proxy , a sort of anonymization proxy to use with LLM providers. It does model (OpenAI privacy filter) + regex PII detection, and replaces them back-and-forth for API requests and responses. With locally hosted detection model, no PII leaves your local environment. I find it very useful especially when you're working on sensitive documents (legal, tax, immigration…

This is very cool because it allows you to use any model. Obviously, it still lets the model and its operator see the entire context of the conversation.

I quite like Moxie's Confer[1] approach to just encrypt the whole thing in such a way that no one except the end-user sees the plaintext.

[1] https://confer.to/

Re: OpenAI Privacy Filter

#52

Post by vessenes »

ares623 wrote: post #45

This looks actually useful. But can someone help me understand how you address the non-perfect scores: "Privacy Filter achieves an F1 score of 96% (94.04% precision and 98.04% recall)." How would you actually use this if it can fail redacting 4% of the data. How do you reliably know which 4% failed?

My experience with models that can reach high 90%-ile benchmark rates on tests is that often that last few percentage is arguable, vague, and often experts would disagree. You could try it yourself by training an MNIST classifier and seeing which digits your model inevitably cannot guess -- you'll be like "...wait a minute..."

Anyway, I have no idea what the underlying data here looks like, but I bet it's pretty unusual.

When I was working on my first job out of college, we were given a large contract and told to redact with black Sharpie every name of a company; it was a basic document prep exercise ahead of a strategy session for a competitor. Standard practice was to share general information but not specific. Our redaction error rate on 200 pages of contract was ... not 100%.

Re: OpenAI Privacy Filter

#53

Post by agnishom »

apothegm wrote: post #4
hiAndrewQuinn wrote: post #2

I'm surprised nobody else has commented on this. This is a very straightforward and useful thing for a small locally runnable model to do.

And also something that it’s dangerous to try to do stochastically.

One could chain a regex based system together with this

Re: OpenAI Privacy Filter

#54

Post by freakynit »

pros wrote: post #35
freakynit wrote: post #22

Can someone explaon how can I reconstruct the original entities back if there are, for example, more than one person names?

You cannot — not with the model alone. It gives you spans + types, not identity. You need to do that part yourself after the model runs. The filter gives you spans; for each one, assign a stable ID (PERSON_1, PERSON_2) and keep {PERSON_1: "Harry", PERSON_2: "Ron"} next to the document. Swap IDs in before the LLM call, swap originals back in the reply. Scoping that map to a document/project keeps the same person consi…

Thanks.. I was expecting it would itself return redacted document and this Map ... but, that spans approach works too.. with a bit of effort.

Also, care to share your app link/homepage? I google, but couldn't find it.

Re: OpenAI Privacy Filter

#55

Post by _pdp_ »

We've implemented this type of feature years ago. I can make a couple of comments as a result:

1. Sanitising PII data needs to be de-santised on the client in order to keep the UX somewhat functional. For example, if you say my name is John which get's redact to [NAME] and the model responds with Hi [NAME] it needs to be converted back to Hi John. This means that you need to have a mechanism for reversing PII at the layer where the user is interacting. Of course, that is true if your care about user experience.

2. Redacted PII data is practically useless for most intents and purposes. The model wont be able to do much without some data and there are many things that are considered PII. For simple chat system this is fine. For something more complex where the user needs to interact with the LLM this becomes extremely challenging as the LLM may not be able to do anything at all. There is also the chance of hallucination.

Overall, it is a feature that we support at platform level but it is not something people tend to use due to these limitations.

In my mind the only practical thing to do is to remove some types of PII that represent a security risk and make sure that you use a trusted model that purges PII data as quickly as possible. This will require a very different type of system.

Re: OpenAI Privacy Filter

#56

Post by MassiveQuasar »

stingraycharles wrote: post #49
fzxu22 wrote: post #37

Working on this: https://github.com/KevinXuxuxu/anon_proxy , a sort of anonymization proxy to use with LLM providers. It does model (OpenAI privacy filter) + regex PII detection, and replaces them back-and-forth for API requests and responses. With locally hosted detection model, no PII leaves your local environment. I find it very useful especially when you're working on sensitive documents (legal, tax, immigration…

How does it handle “unredaction” in responses? E.g. let’s say the LLM does something with the document. You redacted its input, so it emits redacted content. Now what?

The way I handled it is by assigning the redacted tag an id which gets translated back to the saved PII in the output.

Re: OpenAI Privacy Filter

#57

Post by doodlebugging »

blfr wrote: post #51
fzxu22 wrote: post #37

Working on this: https://github.com/KevinXuxuxu/anon_proxy , a sort of anonymization proxy to use with LLM providers. It does model (OpenAI privacy filter) + regex PII detection, and replaces them back-and-forth for API requests and responses. With locally hosted detection model, no PII leaves your local environment. I find it very useful especially when you're working on sensitive documents (legal, tax, immigration…

This is very cool because it allows you to use any model. Obviously, it still lets the model and its operator see the entire context of the conversation. I quite like Moxie's Confer[1] approach to just encrypt the whole thing in such a way that no one except the end-user sees the plaintext. [1] https://confer.to/

That looks interesting. I would like to see them update the Privacy Policy and Terms to acknowledge that their service also works with an Apple ID or with another email. At present, it suggests that the only authentication allowed on your end is through Google's GMail.

Re: OpenAI Privacy Filter

#59

Post by [deleted] »

fzxu22 wrote: post #37

Working on this: https://github.com/KevinXuxuxu/anon_proxy , a sort of anonymization proxy to use with LLM providers. It does model (OpenAI privacy filter) + regex PII detection, and replaces them back-and-forth for API requests and responses. With locally hosted detection model, no PII leaves your local environment. I find it very useful especially when you're working on sensitive documents (legal, tax, immigration…

[deleted]
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