FRiPAL: Face recognition in privacy abstraction layer

Data-driven mobile applications are becoming increasingly popular in civilian and law enforcement. RapidGather, for instance, is an smartphone application that collects data from individual, and spreads rapid emergency responses. Image data is widely used in such applications, and machine learning methods could be utilized to analyze the image data. However, people would hesitate to share the data without protecting their privacy. In this paper, we propose to utilize dimensionality reduction techniques for privacy-preserving machine learning in face recognition for the image data. To demonstrate the proposed approach, we implement a client server system, FRiPAL. With extensive experiments, we show that FRiPAL is efficient, and could preserve the privacy of data owners while maintaining the utility for data users.