Learn how Ente uses on-device machine learning to power private, end-to-end encrypted features like face recognition and semantic search—without a cloud.
Architecture
Overview of Ente's end-to-end encrypted architecture—learn how your data is encrypted on-device, securely shared, and safely stored using cryptography.
Combining Machine Learning and Homomorphic Encryption in the Apple Ecosystem
At Apple, we believe privacy is a fundamental human right. Our work to protect user privacy is informed by a set of privacy principles, and…
Ente Photos: Store and share your photos with absolute privacy
Ente Photos is the private, secure photo storage app with end-to-end encryption. Cross-platform, open source, and self-hostable. Start with 10GB free.
Ente Photos: Store and share your photos with absolute privacy
Ente Photos is the private, secure photo storage app with end-to-end encryption. Cross-platform, open source, and self-hostable. Start with 10GB free.
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.
Private Cloud Compute: A new frontier for AI privacy in the cloud - Apple Security Research
Secure and private AI processing in the cloud poses a formidable new challenge. To support advanced features of Apple Intelligence with larger foundation models, we created Private Cloud Compute (PCC), a groundbreaking cloud intelligence system designed specifically for private AI processing. Built with custom Apple silicon and a hardened operating system, Private Cloud Compute extends the industry-leading security and privacy of Apple devices into the cloud, making sure that personal user data sent to PCC isn’t accessible to anyone other than the user — not even to Apple. We believe Private Cloud Compute is the most advanced security architecture ever deployed for cloud AI compute at scale.
Apple Intelligence Promises Better AI Privacy. Here’s How It Actually Works
Private Cloud Compute is an entirely new kind of infrastructure that, Apple’s Craig Federighi tells WIRED, allows your personal data to be “hermetically sealed inside of a privacy bubble.”
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.
Thread by @matthew_d_green on Thread Reader App
@matthew_d_green: So Apple has introduced a new system called “Private Cloud Compute” that allows your phone to offload complex (typically AI) tasks to specialized secure devices in the cloud. I’m still trying to wo...…
Making end-to-end encrypted AI chat feel like logging in
We want private AI chat to be simple. Yet today, many end-to-end encrypted experiences still have a level of friction that make them feel like they’re from another era: it usually either involves a long seed phrase users are asked to “store securely,” insecure password based encryption, or apps that aren’t cross-device and lose your data periodically (on reinstall, browser cache clear, etc).
zama-ai/concrete-ml
Concrete ML: Privacy Preserving ML framework using Fully Homomorphic Encryption (FHE), built on top of Concrete, with bindings to traditional ML frameworks.
How Google is Making Private AI Practical with Homomorphic Encryption
Today we're excited to showcase HEIR, the latest powerful tool added to our Private Computing Toolkit. HEIR is an open source compiler that unlocks cryptographically-sec…
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.
Confessions to a data lake
I’ve been building Confer: end-to-end encryption for AI chats. With Confer, your conversations are encrypted so that nobody else can see them. Confer can’t read them, train on them, or hand them over – because only you have access to them.
