CHINA UNIONPAY CO., LTD.

Systems designed to streamline and automate various commercial transactions, including mobile-enhanced processes, secure online checkouts, customer service interactions, and privilege issuance, often leveraging digital authentication.

Systems and methods for encrypting data at a fine-grained level (e.g., per data unit or based on sensitivity) and controlling access to it, often involving delegated authorization, contextual policies, or secure data sharing.

Systems and methods for authenticating users, devices, or applications, authorizing their access to resources based on policies, and managing digital identities across various platforms.

Applying artificial intelligence and machine learning techniques to enhance cryptographic systems, such as generating encryption models, improving zero-trust architectures, or enabling privacy-preserving computations like federated learning.

Focuses on using distributed ledger technology (DLT) like blockchain to secure financial transactions, manage digital identities, or ensure data integrity and traceability across various applications.

Mechanisms to facilitate the secure exchange of data between different entities or systems while enforcing usage policies, managing digital content rights, and ensuring data consistency during replication or transfer.

Techniques for distributing computational tasks, data storage, and service logic across cloud data centers, edge devices, and user equipment to improve performance, resilience, or resource utilization. This includes architectures for split rendering, decentralized ledgers, and microservices.

Technologies for securing, managing, and transacting with virtual currencies, non-fungible tokens (NFTs), and other blockchain-based digital assets, including hardware wallets and tokenization schemes for various purposes.

Techniques and systems for optimizing network traffic flow, distributing loads across multiple paths or resources, and ensuring quality of service based on various criteria like application type, latency, or resource availability. This includes dynamic path selection, congestion control, and resource allocation.

Techniques for protecting data at rest or in backup, ensuring its integrity, confidentiality, and verifiable origin, often involving encryption, unique identifiers, or secure repositories.

Methods for training machine learning models across multiple decentralized devices or servers while keeping data localized, often involving aggregation of model parameters and secure communication.

Encompasses strategies and technologies to ensure the availability, integrity, and recoverability of data and systems, including robust backup, replication, error correction, and efficient data restoration.

Developing and applying machine learning algorithms that leverage quantum computing principles, such as quantum circuits or autoencoders, for tasks like simulation or data processing.

Techniques for combining and analyzing information from multiple distinct data modalities (e.g., text, image, video, audio, sensor data) to derive richer insights or improve system performance and decision-making.