Understanding Ethics, Privacy, and Regulations in Smart Video
Surveillance for Public Safety
- URL: http://arxiv.org/abs/2212.12936v1
- Date: Sun, 25 Dec 2022 17:14:18 GMT
- Title: Understanding Ethics, Privacy, and Regulations in Smart Video
Surveillance for Public Safety
- Authors: Babak Rahimi Ardabili, Armin Danesh Pazho, Ghazal Alinezhad Noghre,
Christopher Neff, Arun Ravindran, Hamed Tabkhi
- Abstract summary: This paper focuses on the role of design considering ethical and privacy challenges in Smart Video Surveillance (SVS) systems.
We use several Artificial Intelligence algorithms, such as object detection, single and multi camera re-identification, action recognition, and anomaly detection, to provide a basic functional system.
- Score: 2.4956060473718407
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recently, Smart Video Surveillance (SVS) systems have been receiving more
attention among scholars and developers as a substitute for the current passive
surveillance systems. These systems are used to make the policing and
monitoring systems more efficient and improve public safety. However, the
nature of these systems in monitoring the public's daily activities brings
different ethical challenges. There are different approaches for addressing
privacy issues in implementing the SVS. In this paper, we are focusing on the
role of design considering ethical and privacy challenges in SVS. Reviewing
four policy protection regulations that generate an overview of best practices
for privacy protection, we argue that ethical and privacy concerns could be
addressed through four lenses: algorithm, system, model, and data. As an case
study, we describe our proposed system and illustrate how our system can create
a baseline for designing a privacy perseverance system to deliver safety to
society. We used several Artificial Intelligence algorithms, such as object
detection, single and multi camera re-identification, action recognition, and
anomaly detection, to provide a basic functional system. We also use
cloud-native services to implement a smartphone application in order to deliver
the outputs to the end users.
Related papers
- A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research [45.86892639035389]
This study explores fairness, bias, threats, and privacy in recommender systems.
It examines how algorithmic decisions can unintentionally reinforce biases or marginalize specific user and item groups.
The study suggests future research directions to improve recommender systems' robustness, fairness, and privacy.
arXiv Detail & Related papers (2024-09-19T11:00:35Z) - Privacy-preserving Optics for Enhancing Protection in Face De-identification [60.110274007388135]
We propose a hardware-level face de-identification method to solve this vulnerability.
We also propose an anonymization framework that generates a new face using the privacy-preserving image, face heatmap, and a reference face image from a public dataset as input.
arXiv Detail & Related papers (2024-03-31T19:28:04Z) - Privacy Engineering in Smart Home (SH) Systems: A Comprehensive Privacy Threat Analysis and Risk Management Approach [1.0650780147044159]
This study aims to elucidate the main threats to privacy, associated risks, and effective prioritization of privacy control in SH systems.
The outcomes of this study are expected to benefit SH stakeholders, including vendors, cloud providers, users, researchers, and regulatory bodies in the SH systems domain.
arXiv Detail & Related papers (2024-01-17T17:34:52Z) - Understanding Policy and Technical Aspects of AI-Enabled Smart Video
Surveillance to Address Public Safety [2.2427353485837545]
This paper identifies the privacy concerns and requirements needed to address when designing AI-enabled smart video surveillance.
We propose the first end-to-end AI-enabled privacy-preserving smart video surveillance system that holistically combines computer vision analytics, statistical data analytics, cloud-native services, and end-user applications.
arXiv Detail & Related papers (2023-02-08T19:54:35Z) - PrivHAR: Recognizing Human Actions From Privacy-preserving Lens [58.23806385216332]
We propose an optimizing framework to provide robust visual privacy protection along the human action recognition pipeline.
Our framework parameterizes the camera lens to successfully degrade the quality of the videos to inhibit privacy attributes and protect against adversarial attacks.
arXiv Detail & Related papers (2022-06-08T13:43:29Z) - OPOM: Customized Invisible Cloak towards Face Privacy Protection [58.07786010689529]
We investigate the face privacy protection from a technology standpoint based on a new type of customized cloak.
We propose a new method, named one person one mask (OPOM), to generate person-specific (class-wise) universal masks.
The effectiveness of the proposed method is evaluated on both common and celebrity datasets.
arXiv Detail & Related papers (2022-05-24T11:29:37Z) - Blockchain-based Recommender Systems: Applications, Challenges and
Future Opportunities [2.979263512221363]
blockchain technology is presented as a promising strategy to promote security and privacy preservation in recommender systems.
This paper presents a holistic review of blockchain-based recommender systems covering challenges, open issues and solutions.
arXiv Detail & Related papers (2021-11-22T20:09:38Z) - Privacy and Robustness in Federated Learning: Attacks and Defenses [74.62641494122988]
We conduct the first comprehensive survey on this topic.
Through a concise introduction to the concept of FL, and a unique taxonomy covering: 1) threat models; 2) poisoning attacks and defenses against robustness; 3) inference attacks and defenses against privacy, we provide an accessible review of this important topic.
arXiv Detail & Related papers (2020-12-07T12:11:45Z) - Trustworthy AI Inference Systems: An Industry Research View [58.000323504158054]
We provide an industry research view for approaching the design, deployment, and operation of trustworthy AI inference systems.
We highlight opportunities and challenges in AI systems using trusted execution environments.
We outline areas of further development that require the global collective attention of industry, academia, and government researchers.
arXiv Detail & Related papers (2020-08-10T23:05:55Z) - Digital Surveillance Systems for Tracing COVID-19: Privacy and Security
Challenges with Recommendations [1.506694204377327]
COVID-19 has imposed the public health measure of keeping social distancing for preventing mass transmission of COVID-19.
For monitoring the social distancing and keeping the trace of transmission, we are obligated to develop various types of digital surveillance systems.
This paper discusses the recently designed and developed digital surveillance system applications with their protocols deployed in several countries around the world.
arXiv Detail & Related papers (2020-07-26T17:09:58Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.