Visual sensor networks (VSNs) are receiving a lot of attention in research, and at the same time, commercial applications are starting to emerge. VSN devices come with image sensors, adequate processing power, and memory. They use wireless communication interfaces to collaborate and jointly solve tasks such as tracking persons within the network. VSNs are expected to replace not only many traditional, closed-circuit surveillance systems but also to enable emerging applications in scenarios such as elderly care, home monitoring, or entertainment. In all of these applications, VSNs monitor a potentially large group of people and record sensitive image data that might contain identities of persons, their behavior, interaction patterns, or personal preferences. These intimate details can be easily abused, for example, to derive personal profiles. The highly sensitive nature of images makes security and privacy in VSNs even more important than in most other sensor and data networks. However, the direct use of security techniques developed for related domains might be misleading due to the different requirements and design challenges. This is especially true for aspects such as data confidentiality and privacy protection against insiders, generating awareness among monitored people, and giving trustworthy feedback about recorded personal data—all of these aspects go beyond what is typically required in other applications. In this survey, we present an overview of the characteristics of VSN applications, the involved security threats and attack scenarios, and the major security challenges. A central contribution of this survey is our classification of VSN security aspects into data-centric, node-centric, network-centric, and user-centric security. We identify and discuss the individual security requirements and present a profound overview of related work for each class. We then discuss privacy protection techniques and identify recent trends in VSN security and privacy. A discussion of open research issues concludes this survey.

Security and Privacy Protection in Visual Sensor Networks: A Survey

Widespread use of surveillance cameras in offices and other business establishments, pose a significant threat to the privacy of the employees and visitors. The challenge of introducing privacy and security in such a practical surveillance system has been stifled by the enormous computational and communication overhead required by the solutions. In this paper, we propose an efficient framework to carry out privacy preserving surveillance. We split each frame into a set of random images. Each image by itself does not convey any meaningful information about the original frame, while collectively, they retain all the information. Our solution is derived from a secret sharing scheme based on the Chinese Remainder Theorem, suitably adapted to image data. Our method enables distributed secure processing and storage, while retaining the ability to reconstruct the original data in case of a legal requirement. The system installed in an office like environment can effectively detect and track people, or solve similar surveillance tasks. Our proposed paradigm is highly efficient compared to Secure Multiparty Computation, making privacy preserving surveillance, practical.

/pdf/efficient-privacy-preserving-video-surveillance-6l5zbt5qhl.pdf

Efficient privacy preserving video surveillance

Huge amounts of video are being recorded every day by surveillance systems. Since video is capable of recording and preserving an enormous amount of information which can be used in many applications, it is worth examining the degree of privacy loss that might occur due to public access to the recorded video. A fundamental requirement of privacy solutions is an understanding and analysis of the inference channels than can lead to a breach of privacy. Though inference channels and privacy risks are well studied in traditional data sharing applications (e.g., hospitals sharing patient records for data analysis), privacy assessments of video data have been limited to the direct identifiers such as people's faces in the video. Other important inference channels such as location (Where), time (When), and activities (What) are generally overlooked. In this paper we propose a privacy loss model that highlights and incorporates identity leakage through multiple inference channels that exist in a video due to what, when, and where information. We model the identity leakage and the sensitive information separately and combine them to calculate the privacy loss. The proposed identity leakage model is able to consolidate the identity leakage through multiple events and multiple cameras. The experimental results are provided to demonstrate the proposed privacy analysis framework.

W3-privacy: understanding what, when, and where inference channels in multi-camera surveillance video

Privacy is a big concern in current video surveillance systems. Due to privacy issues, many strategic places remain unmonitored leading to security threats. The main problem with existing privacy protection methods is that they assume availability of accurate region of interest (RoI) detectors that can detect and hide the privacy sensitive regions such as faces. However, the current detectors are not fully reliable, leading to breaches in privacy protection. In this paper, we propose a privacy protection method that adopts adaptive data transformation involving the use of selective obfuscation and global operations to provide robust privacy even with unreliable detectors. Further, there are many implicit privacy leakage channels that have not been considered by researchers for privacy protection. We block both implicit and explicit channels of privacy leakage. Experimental results show that the proposed method incurs 38% less distortion of the information needed for surveillance in comparison to earlier methods of global transformation; while still providing near-zero privacy loss.

https://downloads.hindawi.com/journals/am/2012/639649.pdf

Adaptive transformation for robust privacy protection in video surveillance

The current state of the art and direction of research in computer vision aimed at automating the analysis of CCTV images is presented. This includes low level identification of objects within the field of view of cameras, following those objects over time and between cameras, and the interpretation of those objects’ appearance and movements with respect to models of behaviour (and therefore intentions inferred). The potential ethical problems (and some potential opportunities) such developments may pose if and when deployed in the real world are presented, and suggestions made as to the necessary new regulations which will be needed if such systems are not to further enhance the power of the surveillers against the surveilled.

The future of video analytics for surveillance and its ethical implications

We present a system prototype for self-determination and privacy enhancement in video surveilled areas by integrating computer vision and cryptographic techniques into networked building automation systems. This paper describes research work that has been done within the first half of the collaborative blue-c-II project and is conducted by an interdisciplinary team of researchers. Persons in a video stream control their visibility on a per-viewer base and can choose to allow either the real view or an obscured image to be seen. The parts of the video stream that show a person are protected by an AES cipher and can be sent over untrusted networks. Experimental results are presented by the example of a meeting room scenario. The paper concludes with remarks on the usability and encountered problems.

Privacy in video surveilled areas

We present a system prototype for self-determination and privacy enhancement in video surveilled areas by integrating computer vision and cryptographic techniques into networked building automation systems. This paper describes a system prototype and research work that has been conducted by an interdisciplinary team of researchers. People appearing in a video stream control their visibility on a per-viewer basis and can choose to allow either the real view or an obscured image to be seen. The parts of the video stream containing a person's image are protected by an AES cipher and can be sent over untrusted networks. This paper presents experimental results with the example of a meeting room scenario. We conclude with remarks on the system's usability and on the problems encountered.

Andreas Steffen

Papers

Privacy in video surveilled areas

Privacy in video surveilled spaces