Paper Info
Title
A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analytics.
Abstract
The increasing quality of smartphone cameras and variety of photo editing applications, in addition to the rise in popularity of image-centric social media, have all led to a phenomenal growth in mobile-based photography. Advances in computer vision and machine learning techniques provide a large number of cloud-based services with the ability to provide content analysis, face recognition, and object detection facilities to third parties. These inferences and analytics might come with undesired privacy risks to the individuals. In this paper, we address a fundamental challenge: Can we utilize the local processing capabilities of modern smartphones efficiently to provide desired features to approved analytics services, while protecting against undesired inference attacks and preserving privacy on the cloud? We propose a hybrid architecture for a distributed deep learning model between the smartphone and the cloud. We rely on the Siamese network and machine learning approaches for providing privacy based on defined privacy constraints. We also use transfer learning techniques to evaluate the proposed method. Using the latest deep learning models for Face Recognition, Emotion Detection, and Gender Classification techniques, we demonstrate the effectiveness of our technique in providing highly accurate classification results for the desired analytics, while proving strong privacy guarantees.
Year
Venue
Field
2017
arXiv: Learning
Facial recognition system,Object detection,Social media,Inference,Computer science,Transfer of learning,Human–computer interaction,Artificial intelligence,Deep learning,Analytics,Machine learning,Cloud computing
DocType
Volume
Citations 
Journal
abs/1703.02952
15
PageRank 
References 
Authors
0.59
32
6
Name
Order
Citations
PageRank
Seyed Ali Ossia1232.09
Ali Shahin Shamsabadi2150.92
Ali Taheri3302.18
Hamid R. Rabiee433641.77
Nic Lane5150.59
Hamed Haddadi622322.94