GitHub - Columbia-NLP-Lab/PAPILLON: Code for our paper PAPILLON: PrivAcy Preservation from Internet-based and Local Language MOdel ENsembles

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...

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.

The Consent Layer: Using ligatures to make web text expensive to scrape without asking

ShieldFont is an open-source creative technology project that offers a practical opt-out from unauthorized AI training and disrupts what is collected when that choice is ignored. It swaps 45.8% of content words (around 24.4% of all words) in a page's source code for other (partially) random words, while the font restores the original text on screen. Readers see the work as intended; mass scrapers collect an altered version. In testing, shielding caused over 90% of pages that would otherwise pass the quality filter to be rejected, keeping them out of the training pipeline. Of those that still passed, 19.4% of all words conveyed false meaning, adding noise to unauthorized AI training datasets. This paper's goal is to walk newcomers through the whole process, in plain language and in order: the project's rationale, how it was built, the results, how to deploy it, and where to contribute.

Project Proposal - Social-PETs.EU | Notion

🇪🇺 EU Horizon Call: Privacy Enhancing Technologies

Introducing OpenAI Privacy Filter

OpenAI Privacy Filter is an open-weight model for detecting and redacting personally identifiable information (PII) in text with state-of-the-art accuracy

Permissioned Data PDS Lexicons

I wanted to share a first pass of the PDS lexicons for the permissioned data protocol to get some feedback while the paint’s still wet! You can check them out on my working branch: Comparing main...permissioned-data · bluesky-social/atproto · GitHub They’re all under the com.atproto.space namespace: atproto/lexicons/com/atproto/space at permissioned-data · bluesky-social/atproto · GitHub Confidence rating on Lexicons is probably like ~70-80%. Broad strokes, I think the shape is there. Though ...

fullmoon: local intelligence

chat with private and local large language models

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.

Forcing Generative Models to Degenerate Ones: The Power of Data...

Growing applications of large language models (LLMs) trained by a third party raise serious concerns on the security vulnerability of LLMs.It has been demonstrated that malicious actors can...

Permissioned Spaces Made Pretty — Lexidraw

A drawing by @essentialrandom.bsky.social on Lexidraw

Evaluating permissioned spaces for community contexts - meri's missives

A look at Bluesky's proposed approach to private data on ATProto

greensky: what does permissioned data feel like? - building [at] habitat