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Anand Vidyashankar, a professor in the Department of Statistics at George Mason University, studies the intersection of privacy, security, and the probabilistic foundations of modern AI systems. Trained as a probabilist, he obtained his PhD in mathematics and statistics from Iowa State University and began his career in theoretical probability before spending time in industry at Eli Lilly and Abbott Laboratories, where he worked on statistical modeling and data integrity in regulatory environments. He later extended his work to data privacy and security during his time at the University of Georgia, Cornell University, and now George Mason.
At George Mason, his research broadened to include the legal and operational aspects of statistical privacy—developing frameworks that help organizations translate mathematical guarantees into verifiable compliance procedures. His work connects the statistical underpinnings of privacy and security, introducing quantitative metrics that link the two. “How do you measure it? How do you model it? How can policies and operational practices be aligned with rigorous statistical principles?” These questions guide his research program.
A recurring theme in his work is the misconception that privacy can be achieved simply by removing identifiers such as names or birthdates. In reality, data points across multiple sources can be recombined to reveal sensitive information. His research explores how organizations can share or aggregate data responsibly while balancing analytic utility and individual privacy.
One current project focuses on developing a privacy-preserving sub-agent architecture for the healthcare industry—an intermediary layer that enables secure data sharing while providing stronger privacy guarantees than those offered by large commercial AI systems. This includes applications to genomic and genealogical data, where privacy concerns are especially acute.
Vidyashankar hopes his work will help people better understand how their data moves through the world and how to keep it safe without stifling discovery. In a time when privacy can feel like an illusion, he’s working to make it a measurable, manageable reality.