Wednesday, February 13, 2019 12:15 PM EST
Chang Ge, PhD candidate
David R. Cheriton School of Computer Science
Organizations are increasingly interested in allowing external data scientists to explore their sensitive datasets. Due to the popularity of differential privacy, data owners want the data exploration to ensure provable privacy guarantees. However, current systems for differentially private query answering place an inordinate burden on the data analysts to understand differential privacy, manage their privacy budget and even implement new algorithms for noisy query answering. Moreover, current systems do not provide any guarantees to the data analyst on the quantity they care about, namely accuracy of query answers.
Monday, January 14, 2019 10:30 AM EST
Speaker: Verena Kantere, University of Ottawa
Abstract: Big Data analytics in science and industry are performed on a range of heterogeneous data stores, both traditional and modern, and on a diversity of query engines. Workflows are difficult to design and implement since they span a variety of systems. To reduce development time and processing costs, some automation is needed. In this talk we will present a new platform to manage analytics workflows.
