Security Protection of Biometric Technology for All-in-One Computer Touch Screens: Technological Evolution, Practical Challenges, and Future Pathways
In the era of the Internet of Everything, all-in-one computer touch screens, as the core terminals for human-computer interaction, are deeply integrated into scenarios such as smart homes, industrial control, and smart healthcare. From access control systems to medical equipment operation, and from smart home appliance control to public transportation payment, biometric technology has become the mainstream solution for identity authentication on all-in-one computer touch screens due to its uniqueness, convenience, and high security. However, with the exponential growth of biometric data, security risks such as data breaches, algorithmic vulnerabilities, and privacy misuse are becoming increasingly prominent. How to build a comprehensive protection system covering "accurate recognition - secure transmission - encrypted storage - controllable application" has become a key proposition for the technological evolution of all-in-one computer touch screens.
Biometric technology has evolved from "static features" to "dynamic behaviors" and from "single modality" to "multimodal fusion." Early fingerprint recognition relied on optical sensors and was susceptible to stains and wear; face recognition was limited by lighting and angles, resulting in a relatively high false acceptance rate; and voiceprint recognition faced interference from environmental noise. With technological breakthroughs, technologies such as 3D imaging, thermal imaging, and near-infrared multispectral imaging have effectively addressed lighting issues. For example, multi-light source face recognition technology based on active near-infrared images has surpassed traditional solutions in terms of accuracy and stability.
Multimodal fusion has become a mainstream trend. By integrating multiple biometric features such as fingerprints, faces, irises, and voiceprints, the system can cross-verify identities and significantly enhance security. For instance, in the financial sector, a certain bank's ATMs employ triple authentication using "fingerprint + face + dynamic password," reducing the risk of fraudulent transactions to one in a million. In industrial scenarios, the USR-SH800 all-in-one computer touch screen ensures that only authorized personnel can operate high-risk equipment through "face + voiceprint" dual-factor authentication.
The hardware security of all-in-one computer touch screens forms the foundation of biometric protection. Taking the USR-SH800 as an example, it is equipped with an RK3568 quad-core 64-bit ARM architecture CPU with a main frequency of 2.0GHz and integrates an NPU with 1.0 TOPS of computing power, enabling the localized operation of deep learning models and avoiding the risks associated with uploading biometric data to the cloud. Meanwhile, the device incorporates a built-in security encryption chip that supports the Chinese national cryptographic algorithms SM2/SM4, providing hardware-level encrypted storage for fingerprint, face, and other template data. Even if the device is physically disassembled, the data remains unreadable.
Additionally, the 10.1-inch touch screen of the USR-SH800 adopts a capacitive sensor and supports liveness detection, distinguishing between real fingers and silicone fingerprint films to effectively prevent counterfeit attacks. Its design with four RS485 ports, two network ports, and two USB ports supports the collaborative operation of multiple sensors. For example, in access control systems, it can simultaneously integrate cameras and infrared sensors to achieve composite verification through "face recognition + temperature detection + behavior analysis."
Biometric data belongs to sensitive personal information, and its collection should adhere to the principle of "minimum necessity." For example, in smart home scenarios, a certain brand of smart door locks only collects partial feature points of fingerprints rather than complete images, ensuring that even if the data is leaked, attackers cannot reconstruct the original fingerprints. The edge computing capability of the USR-SH800 enables localized data processing. For instance, in access control systems, the device only uploads identification markers indicating "authentication success/failure" rather than raw face images, reducing data exposure risks at the source.
Furthermore, dynamic de-identification technology can further protect privacy. For example, in medical scenarios, patient face images are immediately converted into feature vectors after collection, and subsequent verification only uses vector data to avoid plaintext storage. The built-in WukongEdge edge application of the USR-SH800 supports customizable de-identification rules, allowing users to flexibly configure them based on scenario requirements.
Biometric data is vulnerable to interception during transmission and requires end-to-end encryption technology. For example, in public transportation payment scenarios, a certain subway system encrypts face data using the TLS 1.3 protocol, combined with a dynamic key distribution mechanism, ensuring that each transmission uses a unique key with an extremely short validity period. The USR-SH800 supports five PLC programming protocols and can collaborate with security gateways to establish a three-layer encrypted channel consisting of "device - gateway - cloud," ensuring that even if a single point is compromised, the data remains undecryptable.
Storage security poses another major challenge. Traditional solutions concentrate biometric data storage in the cloud, which, if breached, could have catastrophic consequences. Distributed storage and blockchain technology offer new approaches. For example, a certain financial platform divides user fingerprint features into multiple segments and stores them in databases across different nodes, with records stored on the blockchain. Any data modification requires consensus from most nodes, effectively preventing tampering attacks. The 4G + 32G memory configuration of the USR-SH800 supports localized storage of critical data, combined with a timed synchronization mechanism to achieve "local - cloud" dual backups, balancing security and availability.
Biometric algorithms face various attack methods, such as deepfakes and adversarial sample attacks. For example, attackers can generate fake face images using Generative Adversarial Networks (GANs) to bypass face recognition systems. To counter such threats, algorithms need to incorporate liveness detection, texture analysis, and other technologies. For instance, a certain security system can identify counterfeit attacks such as high-definition photos and 3D masks by analyzing the micro-texture features of facial skin, achieving an accuracy rate of 99.9%.
The built-in OpenPLC runtime of the USR-SH800 strictly adheres to the IEC61131-3 standard and supports user-defined algorithm modules. For example, in industrial scenarios, users can deploy deep learning models based on attention mechanisms to focus on analyzing the hand movement characteristics of operators, preventing fingerprint film impersonation attacks. Meanwhile, the device supports remote firmware upgrades, enabling timely repair of algorithmic vulnerabilities and response to new attack methods.
In the future, biometric technology will be deeply integrated with technologies such as the Internet of Things (IoT), big data, and AI to build a closed-loop system of "perception - recognition - decision-making - feedback." For example, in smart healthcare scenarios, the USR-SH800 can connect to sensors such as electrocardiograms and blood oxygen monitors, providing multi-dimensional services such as "identity authentication + health monitoring + risk warning" by analyzing patients' physiological signals and behavioral characteristics. Meanwhile, the device supports Node-RED low-code development, enabling hospitals to quickly customize personalized applications, such as designing dual authentication schemes using "voiceprint + gait" for elderly patients to reduce the risk of misoperation.
A single technology cannot address complex security threats, necessitating the construction of a full-stack protection system covering "hardware - algorithms - protocols - management." For example, in the financial sector, a certain bank adopts a solution combining "SE security chip + multimodal fusion + blockchain evidence storage": the SE chip implements hardware encryption of data, multimodal fusion enhances recognition accuracy, and blockchain evidence storage ensures operational traceability. The rich interface design of the USR-SH800 supports such multi-technology collaboration. For example, its two network ports can connect to a security gateway and an audit system respectively, enabling parallel processing of data encryption and behavior monitoring.
With the implementation of regulations such as the Personal Information Protection Law and the Data Security Law, compliance requirements for biometric technology are becoming increasingly stringent. The industry needs to accelerate the development of unified standards, such as defining the collection scope, storage duration, and deletion mechanisms of biometric data. The Linux Ubuntu system of the USR-SH800 supports an open-source ecosystem, allowing users to develop applications based on compliant frameworks such as OpenHarmony to ensure regulatory compliance. Meanwhile, the built-in Nodered tool of the device enables visual process orchestration, helping enterprises quickly build compliant processes, such as automatically triggering data deletion tasks and generating audit logs.
The security protection of biometric technology for all-in-one computer touch screens is a systematic project involving technology, management, and law. From the hardware encryption of the USR-SH800 to multimodal fusion algorithms, and from edge computing to blockchain evidence storage, every innovation is reinforcing security defenses. In the future, with breakthroughs in technologies such as 5G, AI, and quantum computing, biometric technology will embrace broader application prospects. However, only by prioritizing security as its core DNA can it win user trust and promote sustainable industry development. In this endless race, technology enterprises, regulatory agencies, and users must work together to build a secure ecosystem that is "technologically trustworthy, managementally controllable, and legally compliant," enabling biometric technology to truly become the "security key" in the era of the Internet of Everything.
