Join us as we explore the value of data de-identification for organizations and individuals, and the critical factors for ensuring its continued success. We will delve into the common drivers behind de-identification initiatives, and examine two key approaches used in HIPAA compliance: Safe Harbor and Expert Determination. Having de-identified 1,000’s of datasets and led the development of guidance and standards, we will present Expert Determination as an innovative, statistically-informed solution with global applicability (e.g., EU and the GDPR). Through illustrative case studies, we will highlight considerations and best practices that can impact the effectiveness of de-identification efforts. By providing practical insights, our talk will empower organizations to create a sustainable and impactful data de-identification strategy.

About Andrew: Andrew Dickinson joined Privacy Analytics in 2019 and has been involved in the successful delivery of more than a hundred projects of different levels of complexity, types and domains of data, and jurisdictions. He holds a PhD in Computing from Queen’s University, specializing in computer-assisted surgery and has extensive biomedical imaging technology expertise. At Privacy Analytics, he works to develop de-identification strategies that maximize data utility, preserve individual anonymity, and comply with existing regulations.

The world of large language model chatbots like Open AI’s ChatGPT or Google’s Bard is evolving very fast and many people are making haste to integrate these tools into their daily work.  Regrettably, though, many of us work in industries where intellectual property, data privacy, or other compliance concerns prevent us from responsibly going totally wild using these tools in any manner we choose.  Companies are presently figuring out how to react, and I will explore a recent well-publicized decision by Samsung to (temporarily…) ban the use of these tools by employees at work.  I’ll do my best to concretely explore known short-term headaches and probable emerging best practices before speculating on how issues at the intersection of competitive pressures in AI vs. realities of machine learning may make certain of these headaches perennial for a long time.

About Alexander: Alexander Mueller is a mathematician, data scientist, and entrepreneur on a mission to break down the opposition between data privacy and general utility.  Among other things he is a longtime Saint Louisan, eternal Midwesterner, college football and sumo wrestling fan, father, and husband.  His interest is the intersection of AI trends, the competitive pressures they generate, and how emerging technology can solve these problems.