Privacy-Preserving Analytics on the Edge

A Google TechTalk, 2020/7/30, presented by Hamed Haddadi, Imperial College London ABSTRACT: We are increasingly surrounded by applications, connected devices, services, and smart environments which require fine-grained access to various personal data. The inherent complexities of our personal and professional policies and preferences in interactions with these analytics services raise important challenges in privacy. Moreover, due to sensitivity of the data and regulatory and technical barriers, it is not always feasible to do these policy negotiations in a centralized manner. In this talk we present PoliFL, a decentralized, edge-based framework for policy-based personal data analytics. PoliFL brings together a number of existing established components to provide privacy-preserving analytics within a distributed setting. We evaluate our framework using a popular exemplar of private analytics, Federated Learning, and demonstrate that for varying model sizes and use cases, PoliFL is able to perform accurate model training and inference within very reasonable resource and time budgets.