Marina Kurokawa

Title: Risk-based data classification of household energy consumption data from a data protection perspective

Supervisor: Dr. Alexandros Pazaitis, Dr. Christos Giotitsas

Opponent: Dr. Kaija Veskioja

Defense: 6 June 2023


Abstract: Privacy is one of the fundamental human rights, and the concept of data protection was developed to protect personal privacy in a digitized society. With the development of Big Data and algorithms, data are not only direct sources of information but also the basis for interfaces integrating other types of information, including sensitive information. Research to develop methods for inference from electricity consumption data is progressing, and the ability to infer more kinds of information and the accuracy of inferences about such information can be expected to increase in the future as research progresses and is used in conjunction with other data sources. In addition, it has been pointed out that household energy consumption data can be useful for improving private and public services in several ways. On the other hand, energy consumption data sharing through data-sharing platforms is not active due to various problems, and sharing data based on the consent of data subjects without considering the characteristics of the data may lead to privacy violations. With this in mind, how we protect personal identification for ethical and efficient use of household electricity consumption data is critical from a privacy perspective. However, research on household electricity consumption data from a privacy invasion perspective is scarce, except for research on technical system components. And even the GDPR, the EU's comprehensive data protection law, does not explicitly refer to electricity consumption data. This study aims to help understand household electricity consumption data from a privacy perspective and help to fill current research gaps in this area. In this paper, we have explored the recent arguments and challenges for understanding household electricity consumption data from a privacy risk perspective and proposed and applied a new framework to household electricity data.


Keywords: Data protection, privacy, GDPR, energy data, smart meter data