Secure Software and Privacy Engineering
Our research in Secure and Private Software Engineering focuses on the design and development of software systems and applications that integrate security and privacy throughout the entire software development lifecycle. We investigate cutting-edge privacy-enhancing technologies (including data anonymization, synthetic data generation, federated learning, differential privacy, and machine unlearning) and advance secure software engineering practices. We also examine the intersection of software engineering, compliance engineering, and artificial intelligence to ensure that systems are not only technically robust but also aligned with regulatory and ethical obligations.
A core focus of our work is ensuring regulatory compliance in software, translating frameworks such as the GDPR and emerging AI regulations into practical engineering principles. Through privacy engineering, we design systems that uphold user autonomy, promote transparency, and safeguard sensitive information without compromising performance or innovation.
Our research combines academic rigor with real-world applicability, shaping secure and privacy-preserving software practices that meet both industry needs and evolving regulatory expectations.
Our research spans the following interconnected themes:
- Secure Software Engineering: Designing and developing robust, trustworthy, and resilient systems and applications.
- Software Supply Chain Security: Addressing risks in software dependencies, SBOMs, and open-source ecosystems.
- Data Privacy and Data Protection: Embedding privacy-by-design and safeguarding sensitive information across system lifecycles.
- Privacy-Enhancing Technologies (PETs): Developing methods to protect data while enabling system functionality.
- Privacy Engineering: translating privacy principles and regulatory requirements into practical software design, development, and deployment practices.
- Compliance Engineering: Operationalizing frameworks such as GDPR and the EU AI Act into software requirements, design processes, and verification practices to ensure systems are compliant-by-design, rather than adapted after development.
Projects:
Project: Combining the power of Edge Computing and Machine Learning to Enhance Multi-variant Anomaly Detection in IoT
Source: SFI Industry Fellowship Programme 2018
Duration: Oct 2019 - Dec 2020
Project: Improving Cyber Resilience of Mobility-as-a-Service Platforms
Source: TU RISE (Technological Universities Research & Innovation Supporting Enterprise) led by the Higher Education Authority. Funded by Government of Ireland + ERDF
Duration: Sept 2024 - Sept 2028
Contact Us:
We welcome researchers, students, industry partners, and technology practitioners to connect with us and explore collaboration through Innovation Vouchers, Innovation Partnership Programmes, and other research engagement initiatives.
Dr. Vanessa Ayala-Rivera
Vanessa.AyalaRivera@tudublin.ie
School of Informatics and Cybersecurity
Dr. Omar Portillo
Omar.Portillo@tudublin.ie
School of Enterprise Computing and Digital Transformation