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.
> Privacy Filter is a bidirectional token-classification model with span decoding. It begins from an autoregressive pretrained checkpoint and is then adapted into a token classifier over a fixed taxonomy of privacy labels. Instead of generating text token by token, it labels an input sequence in one pass and then decodes coherent spans with a constrained Viterbi procedure.
> The released model has 1.5B total parameters with 50M active parameters.
> [To build it] we converted a pretrained language model into a bidirectional token classifier by replacing the language modeling head with a token-classification head and post-training it with a supervised classification objective.
1. Pass the raw text through the filter to obtain the spans.
2. Map all the spans back to the original text.
Now you have all the PII information.
I'm suggesting that a model designed for high-accuracy redaction can also be used to find all PII in unredacted text. For example, if I don't already know how to find PII (e.g., regex, NLP, etc.) I can use OpenAI's Privacy Filter model to do the work for me.
And because each span has a type (PRIVATE_NAME, etc.) I don't even need to do any work to find only the specific information I am looking for; something that simple diffing wouldn't do.
I'm not saying it's an issue, I just think it is interesting that a tool designed to protect PII can also be used to find it with minimal effort. And it looks like someone already implemented it: https://github.com/chiefautism/privacy-parser.
It works pretty well for the use cases I was playing with.
The OpenAI model is small enough that I might enhance my tool to use it.
I fed it a ~ 100 line markdown document, took about 10 seconds, and it decided that "matter" (as in, frontmatter), "end" (as in, frontend), MCP (as in, mcp server) are organizations.
Most of them don't even make grammatical sense, e.g. "Following the discussion in <PERSON_1>, blahblah".
Brings me back to what NLP was like a decade ago. I always thought spaCy was a very nice project in that space.
It does work better on plain text than markdown because of casing. I can't see what you used (kinda the point - because it run all in your browser) but if you can share the markdown as a gist or something I can take a look and comment more concretely.
Sure they do, computers repeatedly, quickly, and predictably do what they are programmed to do. Which includes any human errors in that programming.
And now they predictably do what they are not programmed to do.
Sure, there's some math that says being really close and exact arn't a big deal; but then you're also saying your secrets don't need to be exact when decoding them and they absolutely do atm.
Sure looks like a weird privacy veil that sorta might work for some things, like frosted glass, but think of a toilet stall with all frosted glass, are you still comfortable going to the bathroom in there?
The use case for this is that many enterprise customers want SaaS products to strip PII from ingested content, and there's no non-model way to do it.
Think, ingesting call transcripts where those calls may include credit card numbers or private data. The call transcripts are very useful for various things, but for obvious reasons we don't want to ingest the PII.
Credit card numbers are deterministic. A five year old could write a script to strip out credit card numbers.
As for other PII ? You're seriously expecting an LLM to find every instance of every random piece of PII ? Worldwide ? In multiple languages ? I've got an igloo I'd like to sell you ...
Since you can't be 100% certain that a filter redacts all personal data, you'd have to make sure that you have measures in place which allow OpenAI to legally process personal data on your behalf. Otherwise you'd technically have a data breach (from a GDPR pov).
And if OpenAI can legally process personal data on your behalf, why bother filtering if processing with filtering is also compliant?
The submission "OpenAI Privacy Filter" that you posted to Hacker News (https://news.ycombinator.com/item?id=47870901) looks good, but hasn't had much attention so far. We put it in the second-chance pool, so it will get a random placement on the front page some time in the next day or so.
This is a way of giving good HN submissions multiple chances at the front page. If you're curious, you can read about it at https://news.ycombinator.com/item?id=26998308 and other links there.Bringing back the Open to OpenAI..
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 consistent across calls, so Harry stays PERSON_1 instead of becoming PERSON_3 the next time he's mentioned.
(Disclosure: I'm building a Mac privacy tool, RedMatiq, that does exactly this. The mapping layer turned out substantially harder than detection.)
For anything touching security or privacy, even small inconsistencies can quickly erode trust.
Even small mistakes can make something dealing with sensitive data hard to trust. It seems useful as a first pass, but I’d probably still want some deterministic checks or a human in the loop to feel confident using it.
Check it out: https://redact.cabreza.com
How would you actually use this if it can fail redacting 4% of the data. How do you reliably know which 4% failed?
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%.