Generalized framework for protecting privacy in the smart grid environment and measuring the efficacy of privacy attacks.

One of the most complex cyber-physical infrastructures is the smart grid, which integrates electricity production, transmission, and consumption with customer realms and millions of connected endpoints. This technology generates a large amount of data and has collected and stored highly sensitive personal information. For this reason, protecting the privacy of data collected by smart grids is important, as it often contains personally identifiable information. Because of this, it is important to give consumers a privacy solution that lets them decide how much information they want to share and what might happen if they do. In this paper, we extend data categorization and sensitivity leveling while simultaneously providing each data attribute with a numerical value. We also propose a generalized methodology based on user-chosen data openness for safeguarding privacy in the context of the smart grid and assessing the effectiveness of privacy attacks. In the end, we developed two algorithms to assess the efficacy of privacy attacks and create a table displaying the findings.