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Abstract: Nearly every team at Facebook depends on the company's custom-built data infrastructure for warehousing and analytics, with roughly 1,000 people across the company -technical and non-technical- using these technologies every day. Given Facebook's unique scalability challenges (their data warehouse is more than 250 PB in size, they add 600 TB of new data every day) and processing needs (they crunch more than 10 PB of data a day), the company's data infrastructure team has to ensure that its systems are prepared to handle not just today's challenges, but tomorrow's as well. In this session, Facebook's Jay Parikh will provide an overview of the company's data infrastructure, focusing on the custom-built technologies they've developed -including Corona, Presto, Morse, and Giraph- to meet the scale challenges they face.

Bio: Jay Parikh is the VP of infrastructure engineering at Facebook. In that role, he leads the engineering and operations teams responsible for building and maintaining an infrastructure that serves more than a billion users, developers, and partners worldwide. Prior to Facebook, Jay was senior vice president of engineering and operations at Ning, where he oversaw the scaling of the company’s social networking platform from 50,000 social networks to more than 1.5 million social networks. Before Ning, Jay was the vice president of engineering at Akamai Technologies, where he helped build the world’s largest and most globally distributed computing platform.

keynote slides
keynote video

Abstract: Privacy-preserving data analysis, also known as statistical disclosure control, has a large literature that spans several disciplines. Many early attempts have proved problematic either in practice or on paper. A new approach, based on the definitional concept of "differential privacy," has provided a theoretically sound and powerful framework that has given rise to an explosion of research. This talk motivates and explains the definition of differential privacy, describes some basic techniques for achieving it, and discusses some of the technical and cultural obstacles to bringing this approach to fruition.

Bio: Cynthia Dwork, Distinguished Scientist at Microsoft Research, is renowned for placing privacy-preserving data analysis on a mathematically rigorous foundation. A cornerstone of this work is differential privacy, a strong privacy guarantee frequently permitting highly accurate data analysis. Dr. Dwork has also made seminal contributions in cryptography and distributed computing, and is a recipient of the Edsger W. Dijkstra Prize, recognizing some of her earliest work establishing the pillars on which every fault-tolerant system has been built for decades. She is a member of the US National Academy of Engineering and a Fellow of the American Academy of Arts and Sciences.