Towards practical, end-to-end differentially private machine learning

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Research Program Ubiquitous Machine Learning (ML) driven applications require ever increasing amounts of data collection, exposing their users to growing privacy risks. Research tools, such as Differential Privacy (DP), exist to leverage the power of ML without undue risks of privacy leakage. However, they are difficult to use at large scale, and their adverse impact on ML algorithms' performance hinders their adoption. The long term objective of this research program is to enable practical, end-to-end deployments of Differential Privacy in ML applications, by building support for Differential Privacy into existing ML infrastructure, and minimizing its impact on the performance of the ML models. Objectives, Methodology, and HQP Realizing this vision requires a varied set of contributions to DP, from new theoretical tools, to new algorithms, and new resource management components, all integrated into existing cluster orchestration systems for ML. For the next five years, I propose three complementary research objectives that together will enable a first practical, turn-key integration of DP into existing ML infrastructure. These research objectives are: 1. designing algorithms for efficient privacy resource management, so that applications training and updating multiple ML models do not overly increase privacy leakage from the collected data ; 2. using data generation techniques to share information between models in a privacy-preserving fashion, improving each model's performance without increasing the overall privacy leakage ; and 3. creating tools to facilitate the development and audit of all ML models produced by an application. Over the five years of the Discovery Grant, I plan to attract and train a diverse group of students to tackle these challenges, including two PhD students and five Master's students. I will also supervise ten Undergraduate research projects. I will foster an inclusive, supportive, and intellectually stimulating lab culture conducive to learning and research progress as a team. Impact Progress along each research objective will advance the state-of-the-art in privacy preserving ML, and will be published in top Computer Science research venues, participating to increasing Canada's attractiveness to HQP in the domain. The anticipated outcome of this proposal, a first end-to-end solution for DP deployments in ML based applications, will make it easier for Canadian companies, non-profits, and government agencies to leverage the power of ML (e.g. in healthcare, education, finance) meeting Canada's strong privacy standards, and lowering the frequency and cost of data breaches. This will in turn allow Canadians to enjoy the promises of ML without undue risks to their privacy. The HQP I will train in the critically demanded skills of ML, systems, and privacy, will be ideal candidates to help build the relevant capabilities in Canada.