Data Privacy (The Derby Mill Series ep 13)

Headquartered in Toronto, Private AI detects and removes personally identifiable information (PII) from data using large language models (LLMs), all without compromising individual or institutional privacy. In this episode, Private AI co-founder and CEO Patricia Thaine offers a behind-the-scenes look at the company’s technical strategy, including the scalability challenge inherent in protecting confidential company information, and the growing threat of re-identification. With more than 30,000 hours invested in building their PII detection system, Private AI now operates in seven countries and partners with organizations such as the Business Development Bank of Canada, MaRS, and the University of Toronto.

This episode features the Intrepid team exploring such questions as:

* How can organizations effectively protect personally identifiable information (PII) and confidential company information in large language models?

* What are the risks of re-identification, even after attempting to anonymize data?

* How can companies balance data utility with privacy preservation?

* How can privacy protection be approached as a dynamic, evolving challenge rather than a static solution?

* What role can technology play in helping organizations understand and control their data privacy?

GUESTS AND HOSTS

Patricia Thaine, co-founder & CEO, Private AIAjay Agrawal, co-founder and partner, Intrepid Growth PartnersRichard Sutton, senior advisor, Intrepid Growth Partners, 2024 Turing Award recipient, pioneer of reinforcement learning and professor, University of AlbertaSendhil Mullainathan, senior advisor, Intrepid Growth Partners, MacArthur Genius grant recipient and professor, MITNiamh Gavin, senior advisor, Intrepid Growth Partners, Applied AI scientist, CEO, Emergent Platforms

LINKS

Private AI website, explainer videoPrivate AI demo, PrivateGPTRead the NYT article, A Face Is Exposed for AOL Searcher No. 4417749Pymetrics, a company that pioneered the use of AI and behavioural science to improve workforce decisions, was acquired by Harver.Rich Sutton’s home page. Follow Rich on XSendhil Mullainathan’s website. Follow Sendhil on XBe sure to catch every episode of The Derby Mill Series by subscribing on the following platforms:YouTube // Spotify // Apple Podcasts

DISCUSSION POINTS

00:00 Introduction01:22 Meet Patricia Thaine of Private AI01:41 About Private AI02:54 How Private AI redacts and protects data04:18 What would scalability look like for confidential company info?08:14 Deconstructing NYT’s article: A Face is Exposed for AOL Searcher No. 441774910:12 Can your digital footprint be an identifier?15:35 Solving the synthetic data problem19:15 How data minimization can help with privacy21:24 Mapping out the future of data privacy25:56 What would a reward function look like?27:12 Final comments

NUGGETS

Nugget 1 - The Challenge of De-Identification Concerning Data Privacy

Private AI CEO and co-founder Patricia Thaine describes the challenge of data privacy and de-identification.

Nugget 2 - Consumer Market Demand and Regulation

Intrepid’s Sendhil Mullainathan explores the challenge of creating a start-up in the “personally identifiable information” space.

Nugget 3 - Different Types of CII and PII

Private AI’s Patricia Thaine discusses the nuances of removing personally identifiable information, as even a piece of jewellery in an X-ray can compromise anonymity.

DISCLAIMER

The content of this podcast is for informational and educational purposes only and should not be construed as marketing, solicitation, or an offer to buy or sell any securities or investments. The opinions expressed in this video are those of the participants and do not necessarily reflect the views of Intrepid Growth Partners or its affiliates. Any discussion of specific companies, technologies, or industries is for illustrative purposes and does not constitute investment advice. Viewers are encouraged to consult with their own financial, legal, and tax advisors before making any investment decisions.

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