Privacy-Preserving Outsourced Feature Extractions in the Cloud: A Survey

Abstract

Large-scale multimedia data generated in our daily life have increasingly motivated companies and researchers to discover valuable knowledge and hidden information by employing machine learning techniques. It is well known that this is an inherently time consuming job to deal with massive multimedia data locally. Thanks to the rapid development of cloud computing, the data owner is highly motivated to outsource these multimedia data, along with the computationally intensive processing tasks, to the cloud by leveraging its abundant resources for cost saving and flexibility. However, due to different trust domains, privacy issues also arise from the exposure to the cloud of private information that is intrinsically embedded in the outsourced data. As a key preprocessing step in machine learning, feature extractions have been extensively studied in the framework of privacy-preserving outsourced computation for their effectiveness in removing irrelevant and redundant data, and increasing learning accuracy. In this article, we provide a comprehensive survey of privacy-preserving outsourcing computation of various feature extraction algorithms in recent literature. Following a brief overview of each scheme, we present the primary technical hurdles needed to be addressed and then compare the state-of-theart solutions in terms of security and effectiveness. Then we discuss several promising future research directions with a focus on privacy-preserving machine learning in the cloud.