Pseudonymization risk analysis in distributed systems

Abstract

In an era of big data, online services are becoming increasingly data-centric; they collect, process, analyze and anonymously disclose growing amounts of personal data in the form of pseudonymized data sets. It is crucial that such systems are engineered to both protect individual user (data subject) privacy and give back control of personal data to the user. In terms of pseudonymized data this means that unwanted individuals should not be able to deduce sensitive information about the user. However, the plethora of pseudonymization algorithms and tuneable parameters that currently exist make it difficult for a non expert developer (data controller) to understand and realise strong privacy guarantees. In this paper we propose a principled Model-Driven Engineering (MDE) framework to model data services in terms of their pseudonymization strategies and identify the risks to breaches of user privacy. A developer can explore alternative pseudonymization strategies to determine the effectiveness of their pseudonymization strategy in terms of quantifiable metrics: i) violations of privacy requirements for every user in the current data set; ii) the trade-off between conforming to these requirements and the usefulness of the data for its intended purposes. We demonstrate through an experimental evaluation that the information provided by the framework is useful, particularly in complex situations where privacy requirements are different for different users, and can inform decisions to optimize a chosen strategy in comparison to applying an off-the-shelf algorithm.

Modern Risk Management Methodologies: Global Experience And Russian Practice

“…As a graphic example, consider the use of risk analysis in one of the leading regional enterprises of Kursk city and Kursk region -JSC the Institute of Ecological Safety (INSTEB). For more effective work, a preliminary self-assessment of JSC INSTEB was carried out using SWOT analysis (Neumann, Grace, Burns, & Surridge, 2019), which resulted in the identification of positive factors and threats to the internal and external environment, as well as the organization potential. In order to provide a more comprehensive presentation of information, we will reflect the results of the conducted research and present the risk analysis in details.…”

The impact of general data protection regulation on software engineering practices

“…Finally, pseudonymization is a process in which there is a replacement of direct and indirect identifiers by coded identifiers that do not allow their interpretation outside the context in which they are used (Starchon and Pikulik, 2019). As Neumann et al (2019) state, pseudonymization contributes to increase data security and may decrease the data caused by possible leaks. This advantage was also mentioned by CS1 as pseudonymized data and their encryption processes are kept separate.…”

Técnicas de anonimización y pseudonimización en la protección de datos personales

“…El cifrado con clave secreta es una técnica aplicada para el reconocimiento de información usando contraseñas de seguridad que permite descifrar valores o datos acerca de un usuario, su eficiencia se relaciona con el compromiso del personal autorizado en proteger los datos y almacenarlos en entornos seguros (Neumann et al, 2019).…”