SafeLiShare: Demystifying Clean Rooms for AI & ML Security

Date & Time: 9am PT, October 26, 2023
Location: Online (Webinar Link to be provided upon registration)

Healthcare Information Exchanges are essential as the federal government continues to promote interoperability and information sharing (e.g., the Trusted Exchange Framework and Common Agreement) and actively enforce information blocking rules, as mandated by the 21st Century Cures Act.

In the ever-evolving landscape of healthcare, the importance of data sharing for informed decision-making and advancing medical research cannot be overstated. However, as we harness the power of data, we must also navigate the intricate terrain of patient privacy and data security. Join us for an insightful webinar as we delve into the critical role of privacy-enhancing technology with real-time asset encryption in healthcare information exchange (HIE) data sharing. We will explore why these technologies are paramount, especially when integrated with machine learning (ML) analytics, to enforce robust third-party data leakage prevention during sharing after data normalization. In an era where healthcare data is both a vital resource and a potential liability, understanding the symbiotic relationship between data sharing, privacy, encryption, and ML analytics is essential for healthcare professionals, researchers, and data custodians alike.

The healthcare industry stands at the nexus of groundbreaking innovations and intricate privacy concerns. As data-driven healthcare becomes increasingly prevalent, the need to strike a balance between data accessibility and patient privacy intensifies. In this webinar, we will unravel the complexities surrounding data sharing in healthcare, focusing on the pivotal role of privacy-enhancing technology and real-time asset encryption. Together, these safeguards fortify the protection of sensitive patient information, even during the most demanding data sharing scenarios. Moreover, we will investigate how the fusion of these technologies with ML analytics can provide proactive measures to thwart third-party data leakage, securing the future of data-driven healthcare. Join us to gain invaluable insights into safeguarding patient privacy while harnessing the full potential of healthcare data for better patient care and medical advancements.

Panelists:

• Anne Anderson, Executive Director of ML Engineering
• Shamim Naqvi, CEO and Co-Founder SafeLiShare

Moderator: Walt Schoenborn, The Science Advisory Board

SafeLiShare Clean Rooms are a cutting-edge platform that you can own (not SaaS) to demystify the complex world of confidential AI and ML modeling and high-risk data handling. These cloud-native secure trusted execution environments (TEEs) are meticulously crafted to facilitate the development of artificial intelligence and machine learning models while safeguarding sensitive information including models and data. In an era where data privacy and security are paramount, SafeLiShare Clean Rooms offer a transparent and structured approach to tackling these challenges.

These Clean Rooms act as a controlled and secure space where data scientists, researchers, and organizations can work on their AI and ML projects with the utmost confidence.

By demystifying the process, they provide a clear and well-defined framework for handling confidential data, ensuring that privacy regulations are adhered to, and intellectual property is protected.

Join this webinar and learn more about how SafeLiShare Clean Rooms can empower users to harness the potential of AI and ML without the ambiguity and uncertainty associated with data privacy concerns.

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