Pitt Cyber has nearly 100 affiliate scholars drawn from across the University. Affiliate scholars are Pitt faculty working on cyber-related transdisciplinary research. Every so often we catch up with one of them on the blog to learn more about what they’re working on.
This week, we spoke with Amin Rahimian, Assistant Professor, Swanson School of Engineering.
Q. You recently received an NSF grant to develop a privacy-preserving framework for research on private, encrypted social networks. Tell us more about your research motivations and what you’re hoping to achieve.
My good friend and collaborator Kiran Garimella at Rutgers is developing data donation tools that give a view of the information ecosystems inside of private, encrypted social networks. My role is to build algorithmic and statistical tools for analyzing the donated data in a privacy-preserving manner. The data donation framework is a fascinating way to study hard-to-reach information ecosystems, e.g., how conspiratorial contents emerge and spread among diaspora communities.
Q. You’re teaching an elective at Swanson this semester titled, Data for Social Good that also includes collaboration for students with the Western Pennsylvania Regional Data Center. What are you hoping students will come away from the course with?
The course is meant to equip our undergraduates with analytics and machine learning skills and encourage them to think broadly about the societal impact of the tools that they are picking up. We discuss bias, fairness, privacy, equity, explainability and other societal risks and opportunities of machine learning in applications such as healthcare, criminal justice, finance, and e-commerce. I hope students will feel empowered to approach data science problems in a socially responsible manner. The collaboration with WPRDC is great for them to test their skills in real-world contexts. All projects deal with our city and county data on bike-friendliness, on gentrification and risks to Naturally Occurring Affordable Housing (NOAH), and on synthetic data of people receiving social services (which is a way to work with sensitive data without compromising individual privacy).
Q. What are you reading now?
I am reading Privacy in Context by Helen Nissenbaum. It helps me think about the paradoxical face of privacy in problem domains such as social learning and social contagion that I am familiar with. The pragmatism and applied nature of Nissenbaum’s contextual integrity framework and its focus on technology make a good fit to the engineering mindset.