scholarly journals Lightweight Frame Scrambling Mechanisms for End-to-End Privacy in Edge Smart Surveillance

2021 ◽  
Author(s):  
Alem Fitwi ◽  
Yu Chen ◽  
Sencun Zhu
Keyword(s):  
Privacy Protection ◽  
Visual Information ◽  
Smart Cities ◽  
Chaotic Map ◽  
Analytic Centers ◽  
Public Network ◽  
Video Frames ◽  
Smart Surveillance ◽  
Protection Mechanisms ◽  
End To End

As smart surveillance becomes popular in today's smart cities, millions of closed circuit television (CCTV) cameras are ubiquitously deployed that collect huge amount of visual information. All these raw visual data are often transported over a public network to distant video analytic centers. This increases the risk of interception and the spill of individuals' information into the wider cyberspace that causes privacy breaches. The edge computing paradigm allows the enforcement of privacy protection mechanisms at the point where the video frames are created. Nonetheless, existing cryptographic schemes are computationally unaffordable at the resource constrained network edge. Based on chaotic methods we propose three lightweight end-to-end (E2E) privacy-protection mechanisms: (1) a Dynamic Chaotic Image Enciphering (DyCIE) scheme that can run in real time at the edge; (2) a lightweight Regions of Interest (RoI) Masking (RoI-Mask) scheme that ensures the privacy of sensitive attributes on video frames; and (3) a novel lightweight Sinusoidal Chaotic Map (SCM) as a robust and efficient solution for enciphering frames at edge cameras. Design rationales are discussed and extensive experimental analyses substantiate the feasibility and security of the proposed schemes.

scholarly journals Lightweight Frame Scrambling Mechanisms for End-to-End Privacy in Edge Smart Surveillance

2021 ◽  
Author(s):  
Alem Fitwi ◽  
Yu Chen ◽  
Sencun Zhu
Keyword(s):  
Privacy Protection ◽  
Visual Information ◽  
Smart Cities ◽  
Chaotic Map ◽  
Analytic Centers ◽  
Public Network ◽  
Video Frames ◽  
Smart Surveillance ◽  
Protection Mechanisms ◽  
End To End

As smart surveillance becomes popular in today's smart cities, millions of closed circuit television (CCTV) cameras are ubiquitously deployed that collect huge amount of visual information. All these raw visual data are often transported over a public network to distant video analytic centers. This increases the risk of interception and the spill of individuals' information into the wider cyberspace that causes privacy breaches. The edge computing paradigm allows the enforcement of privacy protection mechanisms at the point where the video frames are created. Nonetheless, existing cryptographic schemes are computationally unaffordable at the resource constrained network edge. Based on chaotic methods we propose three lightweight end-to-end (E2E) privacy-protection mechanisms: (1) a Dynamic Chaotic Image Enciphering (DyCIE) scheme that can run in real time at the edge; (2) a lightweight Regions of Interest (RoI) Masking (RoI-Mask) scheme that ensures the privacy of sensitive attributes on video frames; and (3) a novel lightweight Sinusoidal Chaotic Map (SCM) as a robust and efficient solution for enciphering frames at edge cameras. Design rationales are discussed and extensive experimental analyses substantiate the feasibility and security of the proposed schemes.

scholarly journals End-to-end digital technologies in “smart cities” of Russia

2021 ◽  
Vol 740 (1) ◽  
pp. 012022
Author(s):  
A I Guseva ◽  
V S Kireev ◽  
P V Bochkarev ◽  
I A Kuznetsov ◽  
S A Filippov
Keyword(s):  
Smart Cities ◽  
Digital Technologies ◽  
End To End

scholarly journals Object Detection, Distributed Cloud Computing and Parallelization Techniques for Autonomous Driving Systems

2021 ◽  
Vol 11 (7) ◽  
pp. 2925
Author(s):  
Edgar Cortés Gallardo Medina ◽  
Victor Miguel Velazquez Espitia ◽  
Daniela Chípuli Silva ◽  
Sebastián Fernández Ruiz de las Cuevas ◽  
Marco Palacios Hirata ◽  
...  
Keyword(s):  
Neural Networks ◽  
Cloud Computing ◽  
Path Planning ◽  
Object Detection ◽  
Short Term Memory ◽  
Smart Cities ◽  
Autonomous Vehicle ◽  
Computational Time ◽  
End To End ◽  
On The Road

Autonomous vehicles are increasingly becoming a necessary trend towards building the smart cities of the future. Numerous proposals have been presented in recent years to tackle particular aspects of the working pipeline towards creating a functional end-to-end system, such as object detection, tracking, path planning, sentiment or intent detection, amongst others. Nevertheless, few efforts have been made to systematically compile all of these systems into a single proposal that also considers the real challenges these systems will have on the road, such as real-time computation, hardware capabilities, etc. This paper reviews the latest techniques towards creating our own end-to-end autonomous vehicle system, considering the state-of-the-art methods on object detection, and the possible incorporation of distributed systems and parallelization to deploy these methods. Our findings show that while techniques such as convolutional neural networks, recurrent neural networks, and long short-term memory can effectively handle the initial detection and path planning tasks, more efforts are required to implement cloud computing to reduce the computational time that these methods demand. Additionally, we have mapped different strategies to handle the parallelization task, both within and between the networks.

Deep learning for detecting logic-flaw-exploiting network attacks: An end-to-end approach

2021 ◽  
pp. 1-30
Author(s):  
Qingtian Zou ◽  
Anoop Singhal ◽  
Xiaoyan Sun ◽  
Peng Liu
Keyword(s):  
Deep Learning ◽  
Attack Detection ◽  
Network Data ◽  
Learning Models ◽  
Real World Data ◽  
Network Attacks ◽  
Network Attack ◽  
Public Network ◽  
Network Intrusion ◽  
End To End

Network attacks have become a major security concern for organizations worldwide. A category of network attacks that exploit the logic (security) flaws of a few widely-deployed authentication protocols has been commonly observed in recent years. Such logic-flaw-exploiting network attacks often do not have distinguishing signatures, and can thus easily evade the typical signature-based network intrusion detection systems. Recently, researchers have applied neural networks to detect network attacks with network logs. However, public network data sets have major drawbacks such as limited data sample variations and unbalanced data with respect to malicious and benign samples. In this paper, we present a new end-to-end approach based on protocol fuzzing to automatically generate high-quality network data, on which deep learning models can be trained for network attack detection. Our findings show that protocol fuzzing can generate data samples that cover real-world data, and deep learning models trained with fuzzed data can successfully detect the logic-flaw-exploiting network attacks.

Online Privacy Protection in Japan

Cyber Crime ◽  
2013 ◽  
pp. 814-831
Author(s):  
J. Michael Tarn ◽  
Naoki Hamamoto
Keyword(s):  
Privacy Protection ◽  
Personal Information ◽  
Online Privacy ◽  
Current Status ◽  
Control Mechanisms ◽  
Future Trends ◽  
Research Directions ◽  
Privacy Law ◽  
Protection Mechanisms ◽  
And Control

This chapter explores the current status and practices of online privacy protection in Japan. Since the concept of privacy in Japan is different from that in western countries, the background of online privacy concepts and control mechanisms are discussed. The chapter then introduces Japan’s Act on the Protection of Personal Information along with the privacy protection system in Japan. Following the discussion of the privacy law, Japan’s privacy protection mechanisms to support and implement the new act are examined. To help companies make smooth adjustments and transitions, a four-stage privacy protection solution model is presented. Further, this chapter discusses two case studies to exemplify the problems and dilemmas encountered by two Japanese enterprises. The cases are analyzed and their implications are discussed. The chapter is concluded with future trends and research directions.

scholarly journals Novel experimental and software methods for image reconstruction and localization in capsule endoscopy

2018 ◽  
Vol 06 (02) ◽  
pp. E205-E210 ◽  
Author(s):  
Anastasios Koulaouzidis ◽  
Dimitris Iakovidis ◽  
Diana Yung ◽  
Evangelos Mazomenos ◽  
Federico Bianchi ◽  
...  
Keyword(s):  
3D Reconstruction ◽  
Capsule Endoscopy ◽  
Visual Information ◽  
Estimation Algorithm ◽  
Absolute Error ◽  
Cylindrical Shape ◽  
Surface Geometry ◽  
Video Frames ◽  
Reconstruction Methods ◽  
Technological Limitations

Abstract Background and study aims Capsule endoscopy (CE) is invaluable for minimally invasive endoscopy of the gastrointestinal tract; however, several technological limitations remain including lack of reliable lesion localization. We present an approach to 3D reconstruction and localization using visual information from 2D CE images. Patients and methods Colored thumbtacks were secured in rows to the internal wall of a LifeLike bowel model. A PillCam SB3 was calibrated and navigated linearly through the lumen by a high-precision robotic arm. The motion estimation algorithm used data (light falling on the object, fraction of reflected light and surface geometry) from 2D CE images in the video sequence to achieve 3D reconstruction of the bowel model at various frames. The ORB-SLAM technique was used for 3D reconstruction and CE localization within the reconstructed model. This algorithm compared pairs of points between images for reconstruction and localization. Results As the capsule moved through the model bowel 42 to 66 video frames were obtained per pass. Mean absolute error in the estimated distance travelled by the CE was 4.1 ± 3.9 cm. Our algorithm was able to reconstruct the cylindrical shape of the model bowel with details of the attached thumbtacks. ORB-SLAM successfully reconstructed the bowel wall from simultaneous frames of the CE video. The “track” in the reconstruction corresponded well with the linear forwards-backwards movement of the capsule through the model lumen. Conclusion The reconstruction methods, detailed above, were able to achieve good quality reconstruction of the bowel model and localization of the capsule trajectory using information from the CE video and images alone.

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