Please note: This PhD defence will take place online.
Chang Ge, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Ihab Ilyas
Data analytics is being widely used not only as a business tool, which empowers organizations to drive efficiencies, glean deeper operational insights and identify new opportunities, but also for the greater good of society, as it is helping solve some of the world’s most pressing issues, such as developing COVID-19 vaccines, fighting poverty and climate change. Data analytics is a process involving a pipeline of tasks over the underlying datasets, such as data acquisition and cleaning, data exploration and profiling, building statistics and training machine learning models. In many cases, conducting data analytics faces two practical challenges. First, many sensitive datasets have restricted access and do not allow unfettered access; Second, data assets are often owned and stored in silos by multiple business units within an organization with different access control. Therefore, data scientists have to do analytics on private and siloed data.
There is a fundamental trade-off between data privacy and the data analytics tasks. On the one hand, achieving good quality data analytics requires understanding the whole picture of the data; on the other hand, despite recent advances in designing privacy and security primitives such as differential privacy and secure computation, when naively applied, they often significantly downgrade tasks’ efficiency and accuracy, due to the expensive computations and injected noise, respectively. Moreover, those techniques are often piecemeal and they fall short in holistically integrating into end-to-end data analytics tasks.
In this thesis, we approach this problem by treating privacy and utility as constraints on data analytics. First, we study each task and express its utility as data constraints; then, we select a principled data privacy and security model for each task; and finally, we develop mechanisms to combine them into end-to-end analytics tasks. This dissertation addresses the specific technical challenges of trading off privacy and utility in three popular analytics tasks: Data Exploring, Sampling, and Profiling.
200 University Avenue West
Waterloo, ON N2L 3G1