A practical overview of privacy preserving deep learning in healthcare

Most healthcare data is scattered across many institutions, making it hard for researchers to access and use—especially given issues like incomplete, delayed, and insufficient demographic information. Instead of trying to aggregate all this data into a single, centralized source—which can be both impractical and risky—new approaches like split learning offer a secure way to analyze data where it already resides. Health Information Exchanges (HIEs), which hold comprehensive, localized patient data, become valuable resources when used with split learning. This method allows collaborative model training without sharing raw, sensitive data. As a result, researchers can study emerging health challenges—such as COVID-19—more effectively, tapping into rich, diverse data sources while protecting patient privacy.

In this talk, Sravan will discuss privacy preserving deep learning techniques, their uses and practical implications of using one. Sravan will lead the audience through understanding the current landscape, and delve deep into understanding split learning for AI development, its architecture and deployment scenarios in regards to the healthcare and HITRUST requirements.

Speaker bio:

Sravan Elineni is a seasoned technologist with over 13 years of expertise in Healthcare Data Systems, Pharmaceutical GxP, Machine Learning, Robotics, Computer Vision, and Natural Language Processing (NLP). Throughout his career, he has consistently exceeded key performance indicators (KPIs) by innovatively designing, developing, and maintaining scalable software and hardware solutions.

Recently, Mr. Elineni led a high-impact project, spearheading the development of a state-of-the-art collaborative machine learning system. This system integrates legal, technological, and security frameworks to facilitate cross-organizational healthcare collaboration, addressing global health challenges such as pandemics and complex healthcare issues.

In addition to his project leadership, Elineni co-founded a company focused on simplifying hardware deployments, particularly in robotics, for various industries. Combining his deep technical background with strategic leadership and entrepreneurial spirit, Elineni excels in driving complex projects, resolving cross-functional challenges, and delivering transformative solutions that bridge technology with real-world applications.

Agenda:

• 6:30 PM - Please join us in the Alessi Kitchen (La Cucina).

• 7:00 PM - Meet in the dining room for our featured presentation followed by discussion. The presentation will last for 45 minutes.

Afterwards - Feel free to reconvene to continue the discussion as a group in either the meeting room or kitchen area.