Deep Visual Privacy Preserving for Internet of Robotic Things
Title | Deep Visual Privacy Preserving for Internet of Robotic Things |
Publication Type | Conference Paper |
Year of Publication | 2019 |
Authors | Abbasi, Milad Haji, Majidi, Babak, Eshghi, Moahmmad, Abbasi, Ebrahim Haji |
Conference Name | 2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI) |
Date Published | 13 June 2019 |
Publisher | IEEE |
ISBN Number | 978-1-7281-0872-8 |
Keywords | automobiles, data privacy, Data sharing and distribution, Deep Neural Network, deep video, image segmentation, Information security, Internet of Robotic things, Internet of Things, IoRT, legal access level, legal applications, Metrics, neural nets, privacy, privacy preserving, pubcrawl, resilience, Resiliency, robot vision, robots, Scalability, security of data, service robotic platforms, Service robots, smart city applications, smart city surveillance, Smart Vehicles, surveillance cameras, video data base, video surveillance, visual data, visual information collection, visual privacy preserving, visualization, Web of Robotic Things, WoRT |
Abstract | In the past few years, visual information collection and transmission is increased significantly for various applications. Smart vehicles, service robotic platforms and surveillance cameras for the smart city applications are collecting a large amount of visual data. The preservation of the privacy of people presented in this data is an important factor in storage, processing, sharing and transmission of visual data across the Internet of Robotic Things (IoRT). In this paper, a novel anonymisation method for information security and privacy preservation in visual data in sharing layer of the Web of Robotic Things (WoRT) is proposed. The proposed framework uses deep neural network based semantic segmentation to preserve the privacy in video data base of the access level of the applications and users. The data is anonymised to the applications with lower level access but the applications with higher legal access level can analyze and annotated the complete data. The experimental results show that the proposed method while giving the required access to the authorities for legal applications of smart city surveillance, is capable of preserving the privacy of the people presented in the data. |
URL | https://ieeexplore.ieee.org/document/8735033 |
DOI | 10.1109/KBEI.2019.8735033 |
Citation Key | abbasi_deep_2019 |
- surveillance cameras
- robot vision
- robots
- Scalability
- security of data
- service robotic platforms
- Service robots
- smart city applications
- smart city surveillance
- Smart Vehicles
- Resiliency
- video data base
- video surveillance
- visual data
- visual information collection
- visual privacy preserving
- visualization
- Web of Robotic Things
- WoRT
- IoRT
- data privacy
- Data sharing and distribution
- Deep Neural Network
- deep video
- image segmentation
- information security
- Internet of Robotic things
- Internet of Things
- automobiles
- legal access level
- legal applications
- Metrics
- neural nets
- privacy
- privacy preserving
- pubcrawl
- resilience