Publications

2018

Methods and Tools for GDPR Compliance Through Privacy and Data Protection Engineering

April 2018. Yod-Samuel Martin, Antonio Kung

In this position paper we posit that, for Privacy by Design to be viable, engineers must be effectively involved and endowed with methodological and technological tools closer to their mindset, and which integrate within software and systems engineering methods and tools, realizing in fact the definition of Privacy Engineering. This position will be applied in the soon-to-start PDP4E project, where privacy will be introduced into existent general-purpose software engineering tools and methods, dealing with (risk management, requirements engineering, model-driven design, and software/systems assurance).

The Impact of Artificial Intelligence on Security: a Dual Perspective

November 2018. Avi Szychter, Hocine Ameur, Antonio Kung, Hervé Daussin

This paper analyses the impact of Artificial Intelligence (AI) on security processes.Through the analysis of risk maps (a risk analysis tool), we highlight two opposing views: Beneficial AI and Malicious AI. Beneficial AI focuses on improving security, covering capabilities such assecurity design and testing assistance, system security monitoring, and decision making upon cyber-attacks. Malicious AI focuses on lowering security, covering capabilities such assistance for attack undetectability, or for attack decision making. While we recall means of attacks ranging from enhanced cyber-attacks to social engineering, we also describe ways of integrating AI incompanies and products’ life cycle and reflections about ethics in AI. We then analyze how impacted IoT systems maybe considering the relationships between connected objects and AI models and their use cases. Finally, we conclude with two recommendations: revisiting risk frame-works to integrate AI, and providing recommendations for anethical approach to AI research.

Agile risk management for multi-cloud software development

December 2018. Victor Muntés-Mulero, Oscar Ripollés, Smrati Gupta, Jacek Dominiak, Eric Willeke, Peter Matthews, Balázs Somosköi

Industry in all sectors is experiencing a profound digital transformation that puts software at the core of their businesses. To react to continuously changing user requirements and dynamic markets, companies need to build robust workflows that allow them to increase their agility in order to remain competitive. This increasingly rapid transformation, especially in domains such as Internet of things or cloud computing, poses significant challenges to guarantee high-quality software, since dynamism and agile short-term planning reduce the ability to detect and manage risks. In this study, the authors describe the main challenges related to managing risk in agile software development, building on the experience of more than 20 agile coaches operating continuously for 15 years with hundreds of teams in industries in all sectors. They also propose a framework to manage risks that consider those challenges and supports collaboration, agility, and continuous development. An implementation of that framework is then described in a tool that handles risks and mitigation actions associated with the development of multi-cloud applications. The methodology and the tool have been validated by a team of evaluators that were asked to consider its use in developing an urban smart mobility service and an airline flight scheduling system.

2019

Smart Grid Challenges through the lens of the European General Data Protection Regulation

August 2019. Jabier Martinez, Alejandra Ruiz, Javier Puelles, Ibon Arechalde, Yuliya Miadzvetskaya

The European General Data Protection Regulation (GDPR) was conceived to protect the privacy of individual citizens and manage the movement of personal data. The Smart Grid has the same needs as any privacy-critical system and, compared to the engineering of other architectures, has the peculiarity of being the source of the energy consumption data, which is an indirect means to infer other personal information with potential professional or commercial value. This work looks at the Smart Grid from the perspective of the GDPR, which is especially relevant now given the current growth and diversification of the Smart Grid ecosystem. We contribute a review of existing works showing the importance of energy consumption as valuable personal data, ananalysis of the established Smart Grid Architecture Model regarding GDPR compliance, and a list of technical and legal challenges where we can highlight the challenge of managing the data processing by third parties.

Model-driven Evidence-based Privacy Risk Control in Trustworthy Smart IoT Systems

September 2019. Victor Muntés-Mulero, Jacek Dominiak, Elena González, David Sanchez-Charles

Preventing privacy-related risks in the creation of Trustworthy Smart IoT Systems (TSIS) will be essential, not only because of the growing amount of regulations that impose strict mechanisms to control risks and quality, but also to effectively mitigate the effect of potential threats exploiting vulnerabilities that may jeopardize privacy. While privacy-related risks usually consider assets represented by elements of the functional description of a TSIS, most monitoring efforts are focused on monitoring security aspects related to the system architecture components and it is not straightforward to link the evidences collected through this monitoring systems to functional description elements, making it difficult to use this informationfor privacy-related risk management. In this paper, we propose a methodology for continuous risk management and a model for risks to increase trustworthiness in IoT systems by enabling continuous monitoring of privacy-related risks. Our approach is based in connecting our risk model with a modelling language to describe IoT architectures, such as that proposed in GeneSIS, and Data Flow Diagrams (DFD). With the combination of architecture (technical description) and data flow models (functional description), we enable continuous risk management using monitoring to improve risk assessment related to data protection issues, as required by GDPR.