The smart grid leverages information technology to augment the conventional power grid. The collection of energy-related data is a crucial aspect of the smart grid. The smart metering infrastructure, deployed throughout the smart grid, has the capability to record household energy consumption with a high degree of precision and transmit consumption data to energy providers in real-time. The consumption data can be used to provide insights useful to the optimization of the energy generation, distribution, and consumption processes, and assist in identifying usage patterns. Apart from the benefits offered to energy shareholders, these data can be also shared publicly, adhering to open science and open data principles.
Open science and open data refer to the practices of making scientific research and data publicly available. Open science promotes collaboration, sharing, and openness in scientific research, which includes making scientific literature, data, and methods freely accessible. Adopting open access policy for papers, preprints, and data enables unlimited access to scientific output. Open data, on the other hand, entails making research data accessible for use and reuse by others. Open data aim to enhance transparency and reproducibility in research, as well as facilitate scientific discoveries powered by data. The use of open data enables researchers to build on the work of others and test new theories.
Nevertheless, the availability of open data creates issues over data protection and privacy, particularly when personal information is included. Consequently, the public sharing of data can have a negative effect on consumers’ privacy and security. For example, the data can be exploited to infer personal details, such as people’s daily routines or the presence of particular appliances in a household. Therefore, consumer identification using consumption data is a considerable privacy threat. To mitigate the impact of potential identification threats, the data should be properly anonymized prior to publication.
To this end, anonymization is an approach for protecting privacy by deleting or hiding personally identifiable information from data. The purpose of anonymization is to guarantee that people cannot be identified from the data, either by itself or in conjunction with additional data. Anonymization is a crucial approach for mitigating any issues and threats associated with the sharing of data containing private or sensitive information. There are several anonymization methods that can be applied prior to the publication of the data, including suppression, bucketization, permutation, perturbation, k-anonymity, L-diversity, t-closeness, and differential privacy. These methods can effectively protect the privacy of individuals, while simultaneously enabling analysts and researchers to derive insights.