PhD student @CUBoulder working on computer security & privacy.
Joined April 2016
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Mingye Chen retweeted
隐私是自由
隐私是秩序
隐私是进步
vitalik.eth.limo/general/202…
Mingye Chen retweeted
Doing a bit of a self-experiment.
Goal: use my personal health and travel data to provide personalized diet and exercise recommendations for me, using frontier models but in a way that avoids leaking to them any private information.
Strategy: use a local model (Qwen 3.8 Flash Next) to orchestrate, and use powerful remote models as a tool call to benefit from higher-level thinking and knowledge that the local model does not have.
Three-layer approach to privacy:
Avoid leaking personally identifiable information, or leaking my identity through writing style -> local model writes the queries to the frontier model, not me
Avoid leaking who I am through the payment channel -> zkAPI
Avoid leaking who I am through networking / IP -> Tor
You need all three (and finally we have all three, at least to some extent)
A skill file teaches the local model when and how to construct minimally-data-revealing requests to remote models. Use zkAPI-wrapped-with-Tor as a CLI tool.
And everything works! I got the recommendations back, info from frontier models helped to improve them.
Main deficiencies:
* Tor is really not optimized for request-by-request de-linking, which is the only form of network-layer privacy that really makes sense in today's world (long-running identifiers are too fragile). Probably not private enough, and latency 10-100x higher than it could be, at the same time.
* The skill file's request construction strategies are definitely far from optimal.
* Qwen 3.8 Flash Next is still too slow for comfort. It's comfortably running at 20-30 TPS, but it would only really start to feel fast at 100+
* There is a tradeoff: the more careful you are about what data you give to a remote model, the less it can help you
github.com/ethereum/zkapi/pu…
Mingye Chen retweeted
Quick updates on the Open Anonymity project (@OpenAnonProject)!
1. OA-chat is live! You can now signup: chat.openanonymity.ai
2. We collaborated with @ethereumfndn (@VitalikButerin, @DavideCrapis, @VittoStack, @curryrasul) to develop zkAPI, a new zero-knowledge protocol for accessing gated / paid APIs with unlinkability. It’s like a generalization of OA unlinkable inference that also abstracts payments away. Now also available on OA-chat!
I'm excited because more infrastructure should exist where privacy and sovereignty is foundational and people are in control.
Mingye Chen retweeted
@openclaw @steipete just want to flag that Venice AI has no privacy/security model; they are a middleman sitting between users and AI, observing all plaintext prompts/responses (the definition of an eavesdropper). we've flagged this publicly and have not gotten a response
This quoted post is unavailable.
Mingye Chen retweeted
Can we build a blind, *unlinkable inference* layer where ChatGPT/Claude/Gemini can't tell which call came from which users, like a “VPN for AI inference”?
Yes! Blog post below + we built it into open source infra/chat app and served >15k prompts at Stanford so far. How it helps with AI user privacy:
# The AI user privacy problem
If you ask AI to analyze your ChatGPT history today, it’s surprisingly easy to infer your demographics, health, immigration status, and political beliefs. Every prompt we send accumulates into an (identity-linked) profile that the AI lab controls completely and indefinitely. At a minimum this is a goldmine for ads (as we know now). A bigger issue is the concentration of power: AI labs can easily become (or asked to become) a Cambridge Analytica, whistleblow your immigration status, or work with health insurance to adjust your premium if they so choose.
This is a uniquely worse problem than search engines because your average query is now more revealing (not just keywords), interactive, and intelligence is now cheap. Despite this, most of us still want these remote models; they’re just too good and convenient! (this is aka the "privacy paradox".)
# Unlinkable inference as a user privacy architecture
The idea of unlinkable inference is to add privacy while preserving access to the remote models controlled by someone else. A “privacy wrapper” or “VPN for AI inference”, so to speak.
Concretely, it’s a blind inference middle layer that:
(1) consists of decentralized proxies that anyone can operate;
(2) blindly authenticates requests (via blind signatures / RFC9474,9578) so requests are provably sandboxed from each other and from user identity;
(3) relays prompts over randomly chosen proxies that don’t see or log traffic (via client-side ephemeral keys or hosting in TEEs); and
(4) the provider simply sees a mixed pool of anonymous prompts from the proxies. No state, pseudonyms, or linkable metadata.
If you squint, an unlinkable inference layer is essentially a vendor for per-request, anonymous, ephemeral AI access credentials (for users or agents alike). It partitions your context so that user tracking is drastically harder.
Obviously, unlinkability isn’t a silver bullet: the prompt itself still goes to the remote model and can leak privacy (so don't use our chat app for a therapy session!). It aims to combat *longitudinal tracking* as a major threat to user privacy, and its statistical power increases quickly by mixing more users and requests.
Unlinkability can be applied at any granularity. For an AI chat app, you can unlinkably request a fresh ephemeral key for every session so tracking is virtually impossible.
# The Open Anonymity Project
We started this project with the belief that intelligence should be a truly public utility. Like water and electricity, providers should be compensated by usage, not who you are or what you do with it. We think unlinkable inference is a first step towards this “intelligence neutrality”.
# Try it out! It’s quite practical
- Chat app “oa-chat”: chat.openanonymity.ai/
(<20 seconds to get going)
- Blog post that should be a fun read: openanonymity.ai/blog/unlink…
- Project page: openanonymity.ai/
- GitHub: github.com/OpenAnonymity
