Unlinkable inference is a technique that provably sandboxes your AI requests from each other and from your identity. We discuss its building blocks, applications, and how it fits into the broader landscape of private personal intelligence.
Private inference
When you use an AI service, you’re handing over your thoughts in plaintext. The operator stores them, trains on them, and–inevitably–will monetize them. You get a response; they get everything.
Tinfoil - Private AI
AI that keeps your data private at all times. Fast, powerful, and verifiable, thanks to secure hardware enclaves.
HUGE — AI Without Giving Up Your Privacy
We're building an Agentic AI Platform running on your Device that lives with YOU, isolated from the cloud, fundamentally reimagining the relationship between humans and artificial intelligence through ownership, privacy, and massive context. In an age of capture and control, HUGE sells independence and sovereignty.
Privacy by design: a formal framework for the analysis of architectural choices
The privacy by design approach has already been applied in different areas. We believe that the next challenge in this area today is to go beyond individual cases and to provide methodologies to explore the design space in a systematic way. As a first step in this direction, we focus in this paper on the data minimization principle and consider different options using decentralized architectures in which actors do not necessarily trust each other. We propose a framework to express the parameters to be taken into account (the service to be performed, the actors involved, their respective requirements, etc.) and an inference system to derive properties such as the possibility for an actor to detect potential errors (or frauds) in the computation of a variable. This inference system can be used in the design phase to check if an architecture meets the requirements of the parties or to point out conflicting requirements.
Public AI Inference Utility
A nonprofit, open-source service to make public and sovereign AI models more accessible.
Privatemode AI - The always encrypted AI service
Privatemode is the first AI service that protects the confidentiality of your data end-to-end. Use AI without security and privacy worries.
Confidential Inference via Trusted Virtual Machines
Announcing a new collaborative research paper on Confidential Inference, a set of tools to improve the security of our model weights and of our users' data
Darkbloom — Cost-Efficient Private AI Inference on Verified Macs
Encrypted inference on hardware-verified Apple Silicon. Comparable model performance, operator-blind privacy, and about 50% lower cost.
Darkbloom — Cost-Efficient Private AI Inference on Verified Macs
Encrypted inference on hardware-verified Apple Silicon. Comparable model performance, operator-blind privacy, and about 50% lower cost.
Darkbloom — Cost-Efficient Private AI Inference on Verified Macs
Encrypted inference on hardware-verified Apple Silicon. Comparable model performance, operator-blind privacy, and about 50% lower cost.
Darkbloom Explained
A plain language guide to Darkbloom a private AI inference network that utilizes idle Apple Silicon Mac computing power.
The Open Anonymity Project
The Open Anonymity Project is a research & engineering effort building tools, infrastructure, and user-facing software for AI user privacy.
Gajesh on Twitter / X
TL;DRapple has turn on this switch for everyone to participate in decentralized inferenceppl can rent out their unused compute space and anyone can use this with privacy guarantees https://t.co/LTP4zyjsdt pic.twitter.com/8Dvo7XK8jJ— Gajesh (@gajesh) February 18, 2026
Private Post-Training and Inference for Frontier Models
A technical deep dive of Silo, our local-like privacy stack for cloud-based training and inference of trillion-parameter models.
