In our modern age, communities face several emerging threats to public safety: rising urbanization, increased crime rates and the threat of terrorism. When addressing the combination of constrained law enforcement resources and growing cities, the challenge of ensuring public safety becomes even more difficult. Advancements in technology have allowed for monitoring devices and cameras to make public spaces safer – but this often comes as a cost.
With an installed base of almost 600 million surveillance cameras, China has almost one camera per two people, and outside of China, the most surveilled cities include Delhi, Seoul, Moscow, New York, and London. While beneficial for public safety, this increase in surveillance comes at a significant cost: erosion of personal privacy. Many individuals value their rights to remain anonymous and free from constant monitoring, and the idea that “Big Brother” is watching can create clashes between safety and privacy, leading to 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 at the camera or video management system level, and incorporating generative AI, AI can be very attractive for public safety monitoring.
The most common AI use cases in surveillance systems include perimeter protection and access control. These applications leverage AI tasks such as object detection, segmentation, video metadata and re-identification to rapidly and accurately identify legitimate vs. suspicious or abnormal people or behavior and trigger responses in real time.
AI-powered surveillance systems can offer more nuanced and sophisticated 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 benefit, 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 data 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, leading to serious data breaches and potential data exposure. However, if the data processing is decentralized, and done at the edges of the network, breaches are limited to the specific node being hacked and a massive data breach is more challenging. Additionally, cloud-based data processing systems must comply with a number of data privacy regulations, which impose limitations on how raw data can be analyzed, resulting in 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 security 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 address these challenges, processing data locally on the device itself instead of sending it to a cloud. If data is distributed, each system can adopt different algorithms and capabilities, presenting several advantages from a privacy standpoint.
By processing data on the device, edge AI systems minimize the need to transmit sensitive information over the internet, significantly reducing any risk of interception during transmission. By storing data locally, the risk of a massive cyberattack is limited, as well. If one device is compromised, the scope of the attack can be contained to the device, as opposed to an entire network.
Finally, edge AI also allows for anonymization of data on the device itself. This then simplifies the process of maintaining the essence of data that is being stored. The essence of the data can then be stored on the edge device or in the cloud without exposing PII.
Critically, edge AI can be designed to focus only on specific events. For example, edge AI can be programmed to identify instances of violence or suspicious behavior, without continuous recording of footage, helping to maintain the privacy of individuals in public spaces. Other tools, like bandwidth limitation, can ensure that video files are not continuously sent to the cloud, reducing the risk of data breaches and preserving individual privacy.
However, for edge AI to be effective as a security tool, it must be both efficient and powerful, able 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 associated with cloud-based systems. As these technologies continue to evolve, edge AI will play a crucial role in creating safer public spaces while respecting individuals’ right to remain anonymous, not only enhancing security but also builds trust in systems designed to protect us.
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