Cryptographic methods for privacy aware computing: applications.
Privacy-Aware Live Video Analytics - CUPS · Privacy-Aware Live Video Analytics System Architecture...
1
Privacy-Aware Live Video Analytics System Architecture Preference and Face Training www.privacyassistant.org About the Project Anupam Das, Xiaoyou Wang, Junjue Wang, Martin Degeling, Brandon Amos, Norman Sadeh, Mahadev Satyanarayanan Cloudlet 1 Send Input Video Stream RTFace Beacon IoTA IRR Registration Send Training Video Stream Request User Specific Face Embedding Trained Features Face Training Web Server Send Extracted Face Embedding Denatured Video Stream Privacy Mediator Privacy Mediator VM Privacy Mediator VM Cloudlet 2 Privacy Mediator VM … Denatured Video Stream Edge Analytics Information to Cloud The accuracy of facial recognition has seen significant improvement in recent years with the adoption of deep convolutional neural networks (DNN). Consequently, we are seeing a growth of commercial applications that are now utilizing facial recognition. For example, Facebook’s Moments app uses facial recognition to automatically create and share photo albums with friends and family. Theme parks like Disney also use facial recognition to automatically group pictures into personalized albums for identified park users. While users benefit from facial recognition-based applications such applications also impose privacy concerns. This is specially true in the era of IoT as decreasing costs of cameras and computation devices have enabled large-scale deployments of IoT cameras in places such as schools, company workspaces, restaurants, shopping malls, and public streets. Recent studies have shown that users privacy preferences vary with applications and with that in mind we have designed a privacy-aware live video streaming system. Introduction Demo Setup IoTA enables users to lookup nearby services. It also allows users to provide their privacy preferences for different services. To opt-in/opt-out of facial recognition-based applications users have to provide training data to the system. Main Components: • IoTA: Personalized privacy assistant for IoT • IRR: IoT Resource Registry • OpenFace: Real-time face detection system • RTFace: Image Denaturing Application • Privacy Engine: Privacy Preference Enforcement Engine • Camera : Raspberry Pi 3 with 8MP 1080p video camera (alternatively laptop/smartphone camera) • Video Analytic : Browser based multicast streaming application • IoTA : Android application on OnePlus 2 smartphone • Beacon: BEEKS beacon advertising IRR The demo currently works for multiple people in the same image. However, this is still a work in progress as we are trying to improve the facial recognition system to detect face profiles.
Transcript of Privacy-Aware Live Video Analytics - CUPS · Privacy-Aware Live Video Analytics System Architecture...
Privacy-Aware Live Video Analytics
System Architecture
Preference and Face Training
www.privacyassistant.org
About the Project
Anupam Das, Xiaoyou Wang, Junjue Wang, Martin Degeling, Brandon Amos, Norman Sadeh, Mahadev Satyanarayanan
Cloudlet 1Send Input Video Stream
RTFace
Beacon
IoTA
IRR
Registration
Send Training Video Stream
RequestUser Specific
Face Embedding
TrainedFeatures
Face Training Web Server
Send Extracted Face Embedding
Denatured Video Stream
Privacy Mediator
Privacy Mediator VM
Privacy Mediator VM
Cloudlet 2
Privacy Mediator VM
…
Denatured Video Stream
Edge Analytics
Information toCloud
The accuracy of facial recognition has seen significantimprovement in recent years with the adoption of deepconvolutional neural networks (DNN). Consequently, we areseeing a growth of commercial applications that are now utilizingfacial recognition. For example, Facebook’s Moments app usesfacial recognition to automatically create and share photoalbums with friends and family. Theme parks like Disney alsouse facial recognition to automatically group pictures intopersonalized albums for identified park users.
While users benefit from facial recognition-based applicationssuch applications also impose privacy concerns. This isspecially true in the era of IoT as decreasing costs of camerasand computation devices have enabled large-scale deploymentsof IoT cameras in places such as schools, companyworkspaces, restaurants, shopping malls, and public streets.Recent studies have shown that users privacy preferences varywith applications and with that in mind we have designed aprivacy-aware live video streaming system.
Introduction
Demo Setup
IoTA enables users to lookup nearby services. It also allowsusers to provide their privacy preferences for different services.To opt-in/opt-out of facial recognition-based applications usershave to provide training data to the system.
Main Components:• IoTA: Personalized privacy assistant for IoT• IRR: IoT Resource Registry• OpenFace: Real-time face detection system• RTFace: Image Denaturing Application• Privacy Engine: Privacy Preference Enforcement Engine
• Camera : Raspberry Pi 3 with 8MP 1080p video camera(alternatively laptop/smartphone camera)
• Video Analytic : Browser based multicast streaming application• IoTA : Android application on OnePlus 2 smartphone• Beacon: BEEKS beacon advertising IRR
The demo currently works for multiple people in the sameimage. However, this is still a work in progress as we are tryingto improve the facial recognition system to detect face profiles.
