In our modern times, the community faces several emerging threats to public safety: rising urbanization, rising crime rate, and threats of terrorism. When solving the combination of constraint law enforcement resources and growing cities, it is more difficult to ensure that public safety challenges have become. The advancement of technology makes the monitoring equipment and cameras safer public space-but this is usually a cost.
China has installed nearly 600 million surveillance cameras, and each two people have nearly one camera. Outside China, the most monitoring cities include Delhi, Seoul, Moscow, New York, New York and London. Although it is good for public safety, the increase in this surveillance has paid a lot of cost: the erosion of personal privacy. Many people attach importance to their own rights to maintain anonymity without continuous monitoring. The “Big Brother” is watching the idea of conflict between security and privacy, which leads to a fierce debate between policy makers.
Artificial intelligence technology enhances public safety
Recently, cameras have increasingly incorporated artificial intelligence, and played more and more role in public safety. By integrating AI into a safety system at the level of camera or video management system, and combining AI, AI may be very attractive to public safety monitoring.
The most common AI use cases in monitoring systems include peripheral protection and access control. These applications use AI tasks, such as object detection, subdivision, video metadata, and re -identification to quickly and accurately determine legal and suspicious or abnormal people or behaviors or behaviors and real -time trigger responses.
The AI -driven monitoring system can provide more subtle and complex functions. With artificial intelligence, the surveillance system can be included in the test, identification and response of security events in real time and its accuracy. While enhancing security and ensuring public safety is a benefit, artificial intelligence does have attracted people’s attention to data privacy. Some people expressed their attention to potential abuse of personal identity information. In the place where a large amount of data is merged, it is important to implement strong data protection measures.
Cloud AI is facing privacy challenges
Traditionally, the cloud -based AI solution provides strong processing capabilities by using centralized data centers, but they do provide certain vulnerabilities of data privacy.
When data storage or “static”, centralized storage will become the key goal of cloud systems for network attacks. Bad actors can invade these systems, resulting in severe data leakage and potential data exposure. However, if the data processing is decentralized and the edge of the network is completed, the vulnerability is limited to a specific node invaded by the hacker, and the large -scale data leakage is more challenging. In addition, cloud -based data processing systems must abide by many data privacy regulations, which has limited how to analyze the original data, which leads to limited insights and even potential legal liabilities. EDGE processing only stores and transmits the minimum information, and at the same time allows in -depth insights.
Moving the data to the cloud to the device will generate other vulnerabilities. By intercepting data during transmission, hackers can expose sensitive information and destroy the security of the system.
In general, the cloud data center is a single -point failure. If it is affected, it may affect many cameras.
Edge AI walks between privacy and security
Edge AI provides a fascinating solution to respond to these challenges and process data locally instead of sending it to the cloud. If you distribute data, each system can adopt different algorithms and functions to propose several advantages from the perspective of privacy.
By processing the data on the device, the Edge AI system reduces the needs of transmitting sensitive information through the Internet, which greatly reduces the risk of any interception in the transmission process. By storing data locally, the risk of large -scale network attacks is also limited. If a device is compromised, the attack range can be included on the device, not the entire network.
Finally, Edge AI also allows anonymous data on the device itself. Then, this simplifies the process of maintaining the essence of the data. The essence of the data can then store the essence of the data in the edge device or cloud without exposing PII.
It is important that the design of Edge AI can only focus on specific events. For example, Edge AI can be programmed to determine the instance of violence or suspicious behaviors without recording the lens continuously, which helps to maintain the privacy of individuals in public places. Other tools, such as bandwidth limit, can ensure that the video files will not be sent to the cloud continuously, thereby reducing the risk of data leakage and retaining personal privacy.
However, to make EDGE AI be effective as a security tool, it must be effective and effective, which can maintain cost -friendly and electricity efficiency, and at the same time is still rapidly handling complex algorithms. AI hardware, including Hailo’s professional AI processor and low power, high -computing performance chips, which makes this possible.
Edge AI provides a promising solution for the challenge of balance of public safety and personal privacy. By processing data locally and inherent restrictions on data transmission and storage, Edge AI reduces risk -related risks related to cloud -based systems. With the continuous development of these technologies, Edge AI will respect the right to maintain anonymity while creating more secure public places. It not only enhances security, but also establishes trust in trusting our system.
