Cybersecurity
1
2
3
4
5
Download this chapter (PDF)
More accessible privacy-enhancing technologies to share data

Privacy-enhancing technologies (PETs) protect sensitive information while enabling secure data processing and sharing. The adoption of PETs – such as differential privacy, synthetic data sets to resemble real sensitive data, federated learning, and homomorphic encryption – is being driven by growing data demands in education and research due to the sensitivity of data.

‍

The recent availability of sector-specific solutions has made PETs more accessible, promising to unlock privacy-focused multi-party data sharing and analytics through research initiatives and private/ public collaborations.

‍

Embedding PETs into IT architectures will complement traditional security-by-design approaches with data-centric controls, reducing organisational vulnerability exposure, and defending against unauthorised access.

Maturity

WATCH
PLAN
ACT
Show the other ACT trends
More diversified access to large AI models Increase in co-evolution between hardware and AI Collaboration between humans and AI Democratisation of XR content creation using generative AI Public investments in XR continue to increase Standardisation of data space architectures for secure and trusted data sharing Growing significance of augmented data management Trusted digital recognitions are emerging Digital Trust Frameworks: from hierarchical to distributed trust Emerging dualuse of AI in cybersecurity More accessible privacy-enhancing technologies to share data Rising pressure to prepare for post-quantum cryptography Emergence of measures for secure Internet of Things The growing need for security protocols to work in the cloud Growing investments in the European cloud infrastructure & sovereignty More AI-driven hardware in data centres to manage workloads in the cloud Increasing need for heterogeneous and hybrid computing platforms Trade-offs in accuracy with approximate computing Fixed wireless access Fibre optic networks: strategic infrastructure for the knowledge economy Enhancing quantum computing robustness and potential Quantum sensing integration to other domains

Drivers

Impact

education

Education

  • As education digitises, PETs will be crucial for securing student and staff data in online learning environments. Using cryptographic techniques and federated learning, PETs address privacy and security concerns in learning analytics, mitigating risks, and fostering trust amongst students and teachers in adaptive, data-driven, or personalised education systems.
Research

Research

  • PETs empower researchers with secure access to sensitive or distributed datasets, mitigating privacy, reputation, and strategic risks associated with data sharing. While these technologies promise greater confidence in innovative research, they also increase pressure on institutions to facilitate them with supporting IT infrastructure.
Operations

Operations

  • As educational institutions adopt emerging (AI, edge computing) and maturing (cloud computing) technologies, the need for robust digital trust and data-centric security is intensifying. PETs will become essential towards achieving reliable and transparent security solutions, determining future IT security and regulatory compliance. PETs maturity and interoperability will support sector-wide implementation.
More info about Cybersecurity?
Visit surf.nl
Link SURF icoon