Privacy Policy · Roster

Privacy Principles

Privacy is an essential part of the web. This document provides definitions for privacy and related concepts that are applicable worldwide as well as a set of privacy principles that should guide the development of the web as a trustworthy platform. People using the web would benefit from a stronger relationship between technology and policy, and this document is written to work with both.

Privacy Principles

Privacy is an essential part of the web. This document provides definitions for privacy and related concepts that are applicable worldwide as well as a set of privacy principles that should guide the development of the web as a trustworthy platform. People using the web would benefit from a stronger relationship between technology and policy, and this document is written to work with both.

User Privacy and Large Language Models: An Analysis of Frontier Developers' Privacy Policies

Hundreds of millions of people now regularly interact with large language models via chatbots. Model developers are eager to acquire new sources of high-quality training data as they race to improve model capabilities and win market share. This paper analyzes the privacy policies of six U.S. frontier AI developers to understand how they use their users' chats to train models. Drawing primarily on the California Consumer Privacy Act, we develop a novel qualitative coding schema that we apply to each developer's relevant privacy policies to compare data collection and use practices across the six companies. We find that all six developers appear to employ their users' chat data to train and improve their models by default, and that some retain this data indefinitely. Developers may collect and train on personal information disclosed in chats, including sensitive information such as biometric and health data, as well as files uploaded by users. Four of the six companies we examined appear to include children's chat data for model training, as well as customer data from other products. On the whole, developers' privacy policies often lack essential information about their practices, highlighting the need for greater transparency and accountability. We address the implications of users' lack of consent for the use of their chat data for model training, data security issues arising from indefinite chat data retention, and training on children's chat data. We conclude by providing recommendations to policymakers and developers to address the data privacy challenges posed by LLM-powered chatbots.

User Privacy and Large Language Models: An Analysis of Frontier Developers' Privacy Policies

Hundreds of millions of people now regularly interact with large language models via chatbots. Model developers are eager to acquire new sources of high-quality training data as they race to improve model capabilities and win market share. This paper analyzes the privacy policies of six U.S. frontier AI developers to understand how they use their users' chats to train models. Drawing primarily on the California Consumer Privacy Act, we develop a novel qualitative coding schema that we apply to each developer's relevant privacy policies to compare data collection and use practices across the six companies. We find that all six developers appear to employ their users' chat data to train and improve their models by default, and that some retain this data indefinitely. Developers may collect and train on personal information disclosed in chats, including sensitive information such as biometric and health data, as well as files uploaded by users. Four of the six companies we examined appear to include children's chat data for model training, as well as customer data from other products. On the whole, developers' privacy policies often lack essential information about their practices, highlighting the need for greater transparency and accountability. We address the implications of users' lack of consent for the use of their chat data for model training, data security issues arising from indefinite chat data retention, and training on children's chat data. We conclude by providing recommendations to policymakers and developers to address the data privacy challenges posed by LLM-powered chatbots.

Data Minimisation in Communication Protocols: A Formal Analysis...

With the growing amount of personal information exchanged over the Internet, privacy is becoming more and more a concern for users. One of the key principles in protecting privacy is data...

Customer Commons

We are planning to release more agreements as we approach the finalization of the IEEE P7012 Standard for Machine Readable Personal Privacy Terms . This initiative is currently has a demo #NoStalking, and we encourage your participation and feedback to help shape its development.

A Duty of Loyalty for Privacy Law

Data privacy law fails to stop companies from engaging in self-serving, opportunistic behavior at the expense of those who trust them with their data. This is a

Astral's Blog

On July 8, Anthropic's updated privacy policy takes effect. Users flagged for potential policy violations will be required to upload a government ID, a selfie or video, and a face geometry template — biometric data processed through Persona, a third-party identity verification company backed by Founders Fund.

Privacy and human behavior in the age of information

This Review summarizes and draws connections between diverse streams of empirical research on privacy behavior. We use three themes to connect insights from social and behavioral sciences: people's uncertainty about the consequences of privacy-related behaviors and their own preferences over those consequences; the context-dependence of people's concern, or lack thereof, about privacy; and the degree to which privacy concerns are malleable—manipulable by commercial and governmental interests. Organizing our discussion by these themes, we offer observations concerning the role of public policy in the protection of privacy in the information age.

Why Meta is retreating from encryption

In 2019, Mark Zuckerberg called privacy the future of social networking. Not anymore

Data Minimisation: a Language-Based Approach (Long Version)

Data minimisation is a privacy-enhancing principle considered as one of the pillars of personal data regulations. This principle dictates that personal data collected should be no more than...

Atty Eleti on Twitter / X

We're taking ChatGPT privacy to the next level.OpenAI is putting together a founding team to bring advanced encryption-based privacy to ChatGPT, the OpenAI API, and our future consumer devices.If you are an expert in TEEs and E2EE, or a product engineer who cares deeply about… pic.twitter.com/6X1sDqBt6U— Atty Eleti (@athyuttamre) November 21, 2025

Permissioned data by dholms · Pull Request #94 · bluesky-social/proposals

This is an initial proposal for permissioned data. Details, terminology, and behaviors are all likely to change. For a friendly introduction to the protocol, check my my Leaflets. For discussion, f...