Who is Telefónica Innovación Digital (TID)?
Telefónica Innovación Digital (TID) is part of Telefónica Group, a global telecommunications leader with a legacy spanning over 100 years. With operations in 12 countries and a workforce exceeding 100,000 employees, Telefónica has consistently driven technological advancement and connectivity across the globe. TID represents the group’s spearhead in technology innovation, bridging research and development to create cutting-edge solutions that align with the demands of an increasingly digital and interconnected world.
At TID, the focus is on fostering innovation in platforms, products, and digital services across diverse fields such as video technologies, network API development, artificial intelligence (AI), Web3, the metaverse, and quantum computing. These technological advancements enable the creation of new business opportunities while addressing critical needs for security and sustainability. By positioning itself at the forefront of innovation, TID plays a critical role in transforming emerging technologies into practical and impactful solutions.
Who Is Telefónica Research?
Telefónica Research, an integral part of TID, is a multidisciplinary team of approximately 30 research scientists dedicated to advancing knowledge and technology in critical domains. The team’s expertise spans:
- Network Architecture Design and Optimization: Pioneering efficient and scalable network solutions.
- Machine Learning and Deep Learning: Developing advanced models to tackle complex computational challenges.
- Human-Computer Interaction: Enhancing the usability and accessibility of digital systems.
- Security and Privacy: Addressing critical challenges to protect user data and system integrity.
- Large Language Model (LLM) Personalization: Tailoring AI-driven models to meet specific user and organizational needs.
- Control Tasks and Recommender Systems: Creating algorithms that optimize decision-making and user experiences.
- Quantum Computing and Quantum Machine Learning: Exploring the potential of quantum technologies to revolutionize computation and problem-solving.
Telefónica Research is highly active in both academic and industrial spheres, producing more than 30 publications annually and filing numerous patents. The team’s involvement in over 20 European and national projects highlights its commitment to collaborative innovation. Additionally, the group interfaces with global business units to transfer technologies internally, contributing to the deployment of groundbreaking solutions across Telefónica’s operations. Dissemination and communication activities further solidify Telefónica Research’s reputation as a thought leader in its domains.
What Is TID’s Role in the MEDIATE Project?
The @MEDIATE project is an ambitious European initiative aimed at developing a robust cybersecurity framework for the computing continuum, encompassing IoT devices, edge computing, and cloud infrastructure. Its vision is rooted in addressing the critical security and privacy attributes of this continuum through an advanced, zero-trust architecture. By leveraging federated learning, MEDIATE enables dynamic and decentralized security scrutinization across all levels of the continuum. Security models developed within the project are designed to be updated, redistributed, and reconfigured, ensuring adaptive and comprehensive threat management.
In the MEDIATE project, TID will lead Work Package 5 (WP5), which is dedicated to developing the Overwatch Modules. Within this work package, TID will be responsible for Task 5.1, which involves designing and implementing the Decision Support System (DSS). The DSS Module is a critical component that aggregates data from Sentinels and other Overwatch Modules, analyzing detected vulnerabilities, threats, and attacks to recommend optimal countermeasures.
The challenge of the DSS Module lies in determining the “best countermeasure action,” which involves balancing competing Key Performance Indicators (KPIs) such as network throughput, latency, recovery time objectives (RTO), and recovery point objectives (RPO). These objectives often conflict, requiring the DSS Module to identify trade-offs and propose solutions that achieve an optimal balance.
TID’s expertise in Multi-Objective Deep Reinforcement Learning (MO-DRL) equips it well to address this challenge. MO-DRL extends traditional reinforcement learning to consider multiple, potentially competing objectives simultaneously. This approach enables the DSS Module to dynamically adapt to evolving conditions and provide context-aware recommendations that enhance the security and performance of the computing continuum.