Speaker: Roxana Geambasu
Location: Soda 430-438, Woz Lounge
Date: May 19, 2023
Time: 12-1pm PST
Title:
Managing Privacy as a Computing Resource in User-Data Workloads
Abstract:
In this talk, I present the perspective that user privacy should be recognized as a crucial computing resource in user-data workloads and managed accordingly. These workloads, prevalent in today’s companies, constantly compute statistics or train machine learning models on user data, making these “products” of the data available to internal analysts, external partners, and even the general population. However, these products often leak significant information about individual users. Differential privacy (DP) offers a rigorous way to limit such data leakage by constraining the data products to noisy aggregates. The talk discusses our group’s work over the past few years on (1) designing a multi-dimensional privacy resource using DP to suit common user-data workloads and (2) integrating support for this resource into popular resource management systems like Kubernetes and caching components. This allows for proper management, including monitoring, scheduling, conservation, payment, and identification of bottlenecks for the privacy resource. By treating privacy as a computing resource, we put it on par with other computing resources that are routinely managed in computer systems (such as CPU, GPU, and RAM), and we acknowledge that user-data workloads are consuming something extra than just these traditional resources.
The talk highlights the main lessons I have learned from our experience building these systems. Firstly, considering privacy as a computing resource helps address certain limitations of DP for practical use. Secondly, while DP is close to practical in certain settings, incorporating it into effective systems requires further evolution of DP theory alongside system design. Lastly, I believe the systems research community is uniquely positioned in tackling the remaining challenges of implementing DP in practice, so my talk serves as a call to action for systems researchers to help bring this much needed privacy technology to practice.
Bio:
Roxana Geambasu is an Associate Professor of Computer Science at Columbia University and a member of Columbia’s Data Sciences Institute. She joined Columbia in Fall 2011 after finishing her Ph.D. at the University of Washington. For her work in data privacy, she received: an Alfred P. Sloan Faculty Fellowship, an NSF CAREER award, a Microsoft Research Faculty Fellowship, several Google Faculty awards, a “Brilliant 10” Popular Science nomination, the Honorable Mention for the 2013 inaugural Dennis M. Ritchie Doctoral Dissertation Award, a William Chan Dissertation Award, two best paper awards at top systems conferences, and the first Google Ph.D. Fellowship in Cloud Computing.