Due to the recent advancements in the Internet of Things (IoT) and Edge-Fog-Cloud Computing technologies, the Smart Public Safety (SPS) system has become a more realistic solution for seamless public safety services that are enabled by integrating machine learning (ML) into heterogeneous edge computing networks. While SPS facilitates convenient exchanges of surveillance data streams among device owners and third-party applications, the existing monolithic service-oriented architecture (SOA) is unable to provide scalable and extensible services in a large-scale heterogeneous network environment. Moreover, traditional security solutions rely on a centralized trusted third-party authority, which not only can be a performance bottleneck or the single point of failure, but it also incurs privacy concerns on improperly use of private information. Inspired by blockchain and microservices technologies, this paper proposed a BLockchain-ENabled Decentralized Smart Public Safety (BlendSPS) system. Leveraging the hybrid blockchain fabric, a microservices based security mechanism is implemented to enable decentralized security architecture, and it supports immutability, auditability, and traceability for secure data sharing and operations among participants of the SPS system. An extensive experimental study verified the feasibility of the proposed BlendSPS that possesses security and privacy proprieties with limited overhead on IoT based edge networks.

With a myriad of edge cameras deployed in urban and suburban areas, many people are seriously concerned about the constant invasion of their privacy. There is a mounting pressure from the public to make the cameras privacy-conscious. This paper proposes a Privacy-preserving Surveillance as an Edge service (PriSE) method with a hybrid architecture comprising a lightweight foreground object scanner and a video protection scheme that operates on edge cameras and fog/cloud-based models to detect privacy attributes like windows, faces, and perpetrators. The Reversible Chaotic Masking (ReCAM) scheme is designed to ensure an end-to-end privacy while the simplified foreground-object detector helps reduce resource consumption by discarding frames containing only background-objects. A robust window-object detector was developed to prevent peeping via windows; whereas human faces are detected by using a multi-tasked cascaded convolutional neural network (MTCNN) to ensure de-identification. The extensive experimental studies and comparative analysis show that the PriSE scheme (i) can efficiently detect foreground objects, and scramble those frames that contain foreground objects at the edge cameras, and (ii) detect and denature window and face objects, and identify perpetrators at a fog/cloud server to prevent unauthorized viewing via windows, to ensure anonymity of individuals, and to deter criminal activities, respectively.

The drone technology has been increasingly used by many tech-savvy consumers, a number of defense companies, hobbyists and enthusiasts during the last ten years. Drones often come in various sizes and are designed for a multitude of purposes. Nowadays many people have small-sized personal drones for entertainment, filming, or transporting items from one place to another. However, personal drones lack a privacy-preserving mechanism. While in mission, drones often trespass into the personal territories of other people and capture photos or videos through windows without their knowledge and consent. They may also capture video or pictures of people walking, sitting, or doing private things within the drones’ reach in clear form without their go permission. This could potentially invade people’s personal privacy. This paper, therefore, proposes a lightweight privacy-preserving-by-design method that prevents drones from peeking through windows of houses and capturing people doing private things at home. It is a fast window object detection and scrambling technology built based on image enhancing, morphological transformation, segmentation and contouring processes (MASP). Besides, a chaotic scrambling technique is incorporated into it for privacy purpose. Hence, this mechanism detects window objects in every image or frame of a real-time video and masks them chaotically to protect the privacy of people. The experimental results validated that the proposed MASP method is lightweight and suitable to be employed in drones, considered as edge devices.

Distributed sensors are the eyes and ears of a smart grid which provide information vital for monitoring and controlling the entire power generation, transmission, and distribution systems. Secure exchange of information among the sensing and decision-making entities is essential as failures may bring the entire system on its knees. With the rapid growth in the number of distributed sensors, drones have a myriad of applications. A swarm of drones could also be deployed in war zones and disaster-stricken areas where a secured intercommunication is of paramount importance for survivability and for successful mission completion. In this paper, a secure mechanism is proposed based on mobile agents to secure information exchange with minimum overhead. An Agent Administrator (AA) automatically clones and sends a secure mobile agent (SMA) to the target sensors or drones to scan and check their security status. Then, the dispatched SMAs send feedbacks to the server AA or other members. In case of sensors, the closest terminal unit to which the sensors are directly connected is designated as an AA, which is capable of checking authentication and scanning for vulnerabilities. In the case of drones, any one of them or multiple of them could be designated as the AA and the flagged feedback is broadcast to all other nodes or drones thereby providing them security status updates. A modified Nagle’s Algorithm is also proposed to support real-time video transmission. The experimental results validate the effectiveness and convenience of the proposed system.

Privacy protecting is a very challenging task in a highly surveilled world with zillions of surveillance cameras deployed. The difficulty mainly lies in the facts: (i) there is not a distinctively defined boundary between usability and privacy, (ii) video frames indiscriminately created and collected by the edge cameras could be abused and intercepted, and (iii) it is difficult to enforce the commonly used compute-intensive standard techniques as-is on the edge cameras because of limited computational resources. In this paper, we propose a lightweight and secure scheme to Enforce Privacy-preservation on Edge Cameras (EnPEC) using deep learning and a sinusoidal chaotic-map. The proposed EnPEC architecture comprises a lightweight frame classifier designed to label frames as offensive and harmless depending on their content to ensure the practice of selective surveillance following a frame approximation process and a novel sinusoidal-map-based chaotic image scrambling technique that enciphers frames color-channel wise to ensure end-to-end privacy of frame contents. The extensive analysis of the functionality, performance and security of the EnPEC scheme, and comparison with related works verify that the EnPEC scheme is more feasible, robust and secure when it runs in real-time on edge cameras equipped with computational power equivalent to the Raspberry PI 4.

The pervasive and intrusive surveillance practices have raised a widespread concern amongst zillions of people about the invasion of their privacy. The privacy breaches in the existing mass-surveillance system are mainly attributed to the exploits of vulnerabilities by adversaries and abuse of cameras by people in charge of them. As a result, there has been a tremendously pressing demand from the public to make the surveillance system privacy-conscious. In this paper, we propose PriSev, a privacy-preserving selective video surveillance method, which enables selective-surveillance where only video frames containing aggressive and suspicious behavioral patterns, like gun brandishing or/and fist-raising, are made available for view by security personnel in the surveillance operation center and for storage. By introducing a lightweight dynamic chaotic image enciphering (DyCIE) scheme, the proposed PriSev method enables onsite object detection and frame encryption at the network edge where the video is created. At the fog/cloud layer, frame decryption is efficiently performed followed by deep-neural-network (DNN) based frame-filtering and selective storage that runs on a surveillance server. In addition, a multiagent system is introduced for the exchange of deciphering keys between the sending and receiving agents. Extensive experimental study and performance analyses corroborate that the proposed PriSev method is able to efficiently perform a privacy-preserving selective surveillance in real-time.