In our modern age, communities face several emerging threats to public safety: rising urbanization, increased crime rates and the specter of terrorism. When addressing the mixture of constrained law enforcement resources and growing cities, the challenge of ensuring public safety becomes even harder. Advancements in technology have allowed for monitoring devices and cameras to make public spaces safer – but this often comes as a value.
With an installed base of just about 600 million surveillance cameras, China has almost one camera per two people, and out of doors of China, essentially the most surveilled cities include Delhi, Seoul, Moscow, Recent York, and London. While helpful for public safety, this increase in surveillance comes at a big cost: erosion of non-public privacy. Many individuals value their rights to stay anonymous and free from constant monitoring, and the concept that “Big Brother” is watching can create clashes between safety and privacy, resulting in fierce debates between policymakers.
Artificial Intelligence Technology for Enhanced Public Safety
Recently, cameras have increasingly incorporated artificial intelligence, playing a growing role in public safety. By integrating AI into security systems on the camera or video management system level, and incorporating generative AI, AI might be very attractive for public safety monitoring.
Probably the most common AI use cases in surveillance systems include perimeter protection and access control. These applications leverage AI tasks comparable to object detection, segmentation, video metadata and re-identification to rapidly and accurately discover legitimate vs. suspicious or abnormal people or behavior and trigger responses in real time.
AI-powered surveillance systems can offer more nuanced and complex capabilities. With artificial intelligence, surveillance systems can incorporate detection, identification and response to security events in real-time and with high accuracy. While enhancing security and ensuring public safety is a profit, artificial intelligence does raise concerns about data privacy, with some expressing concern about potential misuse of personally identifiable information. Where there’s large quantities of knowledge being incorporated, it’s critical to implement robust data protection measures.
Cloud AI Faces Privacy Challenges
Cloud-based AI solutions have traditionally offered powerful processing capabilities by leveraging centralized data centers, but they do offer certain vulnerabilities for data privacy.
When data is stored, or “at rest,” centralized storage makes cloud systems key targets for cyberattacks. Bad actors can hack into these systems, resulting in serious data breaches and potential data exposure. Nevertheless, if the info processing is decentralized, and done at the perimeters of the network, breaches are limited to the particular node being hacked and an enormous data breach is tougher. Moreover, cloud-based data processing systems must comply with quite a few data privacy regulations, which impose limitations on how raw data might be analyzed, leading to limited insights and even potential legal liabilities. Edge processing only stores and transmits the minimum required information, while still allowing for profound insights.
Moving data to and from the cloud to devices creates additional points of vulnerability. By intercepting data during transmission, hackers can expose sensitive information and undermine the safety of the system.
Overall, a cloud data center is a single point of failure that, if impacted, could affect many cameras.
Edge AI Walks the Tightrope Between Privacy and Security
Edge AI offers a compelling solution to deal with these challenges, processing data locally on the device itself as an alternative of sending it to a cloud. If data is distributed, each system can adopt different algorithms and capabilities, presenting several benefits from a privacy standpoint.
By processing data on the device, edge AI systems minimize the necessity to transmit sensitive information over the web, significantly reducing any risk of interception during transmission. By storing data locally, the danger of an enormous cyberattack is proscribed, as well. If one device is compromised, the scope of the attack might be contained to the device, versus a complete network.
Finally, edge AI also allows for anonymization of knowledge on the device itself. This then simplifies the means of maintaining the essence of knowledge that’s being stored. The essence of the info can then be stored on the sting device or within the cloud without exposing PII.
Critically, edge AI might be designed to focus only on specific events. For instance, edge AI might be programmed to discover instances of violence or suspicious behavior, without continuous recording of footage, helping to keep up the privacy of people in public spaces. Other tools, like bandwidth limitation, can be sure that video files aren’t repeatedly sent to the cloud, reducing the danger of knowledge breaches and preserving individual privacy.
Nevertheless, for edge AI to be effective as a security tool, it should be each efficient and powerful, in a position to remain cost-friendly and power efficient while still processing complex algorithms quickly. AI hardware, including Hailo’s specialized AI processors and low-power, high-compute performance chips, is making this possible.
Edge AI presents a promising solution to the challenge of balancing public safety with personal privacy. By processing data locally and imposing inherent limitations on data transmission and storage, edge AI reduces the risks related to cloud-based systems. As these technologies proceed to evolve, edge AI will play an important role in creating safer public spaces while respecting individuals’ right to stay anonymous, not only enhancing security but additionally builds trust in systems designed to guard us.
