Privacy

What does privacy mean to us at Private AI? As a tech company whose purpose it is to enhance privacy, we are acutely aware of the value tension between businesses needing access to data to build amazing tools and the privacy interests of individual consumers. The privacy laws that have been enacted all over the … Read more

Large language models (LLMs) are a type of machine learning model that are trained on vast amounts of text data to generate human-like text. They are able to generate text that can be indistinguishable from text written by a human, and can be used for a wide range of natural language processing tasks such as … Read more

In today’s data-driven world, businesses are constantly collecting information from their customers in order to provide a better product or service, to understand and alleviate any pain points along their path to acquisition, to gain insights and create more efficient processes, and so much more. Data is often considered critical for modern organizations, but the … Read more

Sign Up What is the Metaverse? Before we get to privacy in the Metaverse, we first have to define “Metaverse” itseld. Ever since the word hit our vocabularies in 2021, there has been ample confusion with many publications trying their hands at making sense of what it means: “It may even be the case that … Read more

Differential privacy is a hot topic given the many conflicting opinions on its effectiveness. For some background, we previously wrote a comprehensive post on the Basics of Differential Privacy where we discussed the risks and how it can also enhance natural language understanding (NLU) models.  The differential privacy papers in this post are just a … Read more

Over the years, large pre-trained language models like BERT and Roberta have led to significant improvements in natural language understanding (NLU) tasks. However, these pre-trained models pose a risk of potential data leakage if the training data includes any personally identifiable information (PII). To mitigate this risk, there are two common techniques to preserve privacy: … Read more

In today’s world, large models with billions of parameters trained on terabytes of datasets have become the norm as language models are the foundations of natural language processing (NLP) applications. Several of these language models used in commercial products are also being trained on private information. An example would be Gmail’s auto-complete model. Its model … Read more

Previously on Private AI’s speaker series CEO, Patricia Thaine, sat down with Franziska Boenisch to discuss her latest paper, ‘When the Curious Abandon Honesty: Federated Learning Is Not Private’.  Franziska completed a Master’s degree in Computer Science at Freie University Berlin and Technical University Eindhoven. For the past 2.5 years, she has been working at Fraunhofer AISEC as a Research … Read more