This talk starts with a real-world issue: to provide indoor localization services to satisfy contextual and ephemeral needs, e g , at conferences or exhibitions events As such, the costs and requirements of providing the services need to be minimal We design, implement, and evaluate Tack, a new mobile application framework that uses a combination of known landmark locations, contacts over Bluetooth Low Energy, crowdsourcing, and dead-reckoning to estimate and refine user locations At its core, an inference algorithm is designed to run on mobile devices to make the estimation accurate Accuracy and privacy pose as a pair of contradictory requirements in machine learning frameworks -- stricter privacy guarantee is always achieved with degraded learning accuracy -- and such degradation is even worse with deep learning We found the fundamental cause is that a loose characterization of utility and privacy leads to over-distortion of the model By recognizing the accuracy-privacy tradeoff as a utility maximization problem subject to a set of privacy constraints, we lower-bounds the distortion, and significantly improves the learning accuracy as compared to the state-of-the-art under the same privacy guarantee

Liyao Xiang is a final-year Ph.D. student in the Department of Electrical and Computer Engineering, University of Toronto. She received the B.S. degree from the Department of Electronic Engineering at Shanghai Jiao Tong University in 2012, and the M.A.Sc. degree from ECE, University of Toronto in 2015. Her general research interests cover the broad area of security and privacy, privacy analysis in data mining, mobile systems and applications, mobile cloud computing. Particularly, she is interested in identifying fundamental privacy issues in deep learning systems and designing practical mechanisms for protecting data privacy.