A Scalable and Privacy-Aware IoT Service for Live Video Analytics
| Title | A Scalable and Privacy-Aware IoT Service for Live Video Analytics |
| Publication Type | Conference Paper |
| Year of Publication | 2017 |
| Authors | Wang, Junjue, Amos, Brandon, Das, Anupam, Pillai, Padmanabhan, Sadeh, Norman, Satyanarayanan, Mahadev |
| Conference Name | Proceedings of the 8th ACM on Multimedia Systems Conference |
| Publisher | ACM |
| Conference Location | New York, NY, USA |
| ISBN Number | 978-1-4503-5002-0 |
| Keywords | AI, artificial intelligence, cloud computing, cloudlet, edge computing, face recognition, Human Behavior, human factor, human factors, Metrics, privacy, Privacy Mediator, privacy protection, pubcrawl, resilience, Resiliency, Scalability, user behavior, user privacy in the cloud |
| Abstract | We present OpenFace, our new open-source face recognition system that approaches state-of-the-art accuracy. Integrating OpenFace with inter-frame tracking, we build RTFace, a mechanism for denaturing video streams that selectively blurs faces according to specified policies at full frame rates. This enables privacy management for live video analytics while providing a secure approach for handling retrospective policy exceptions. Finally, we present a scalable, privacy-aware architecture for large camera networks using RTFace. |
| URL | http://doi.acm.org/10.1145/3083187.3083192 |
| DOI | 10.1145/3083187.3083192 |
| Citation Key | wang_scalable_2017 |