Abstract

     Significant amounts of data are collected in buildings. While these data have great potential for maximizing the energy efficiency of buildings in general, only a small portion of the data are accessible to researchers, government, and industry for analyses. Concerns about privacy are one of the major barriers prohibiting access to these data. Privacy preservation techniques are generally applied to this problem not only to preserve underlying privacy but also to improve the usefulness of data. Among various privacy preserving techniques, differential privacy has become one of the more popular solutions since its introduction in 2006. Differential privacy is a mathematical measure for protecting privacy so that one’s privacy cannot be incurred by participating in a database. Although significant research improvements have been made for more than a decade, applying differential privacy to data collected in buildings is still an immature field of study. Because implementing differential privacy on a certain use case is not straightforward and can be achieved with various configurations, it is important to understand variation of configurations with different use cases around data collected from buildings. This literature review aims to introduce what has been done to implement differential privacy in data collected in buildings, and to discuss associated challenges and potential future research opportunities.

Introduction

     Background: The residential and commercial buildings sector accounted for 20% of global energy consumption in 2018 [1], and a much higher 39% in the United States in 2019 [2]. Many research efforts are focused on reducing the energy consumption, increasing the energy efficiency of buildings, and reducing carbon emission. Buildings can also provide services to utilities to enable deeper penetration of renewable energy on the grid. One of the major pathways to achieve these goals is to first understand the operational performance of buildings with collected data. The performance reflected in these data either informs the reality of existing buildings, reflects the effects of short- or long-term events and improvements in buildings, or aids the development of innovative approaches for maximizing building energy efficiency. The focus of this literature review aligns with the last use case, where extracting insights from the data greatly benefits further research.

     Definition of privacy: The big data era saw a massive increase in data collection as well as unprecedented privacy threats. It is worth noting what privacy means in this field of study because the definition of privacy has evolved over the years. An early definition of privacy started with “the right to be left alone” [۳]; however, social situations have evolved since then, as mentioned by Hayashi [4] who notes “self-determination such as sexual orientation or contraception is admitted as one of the privacy elements.” Similar evolution also occurred and continues to occur in data collected from buildings. Granular data, such as smart meter data collected from advanced metering infrastructure (AMI) in sub-hourly (e.g., down to 15 min) intervals, has become available (owned by many utility companies around the world), but research has also shown that these data include various types of private information given the strong correlation of occupants (or building operators) with building energy consumption. Thus, there is a need to define privacy, especially for data collected in buildings. While Warren and Brandeis [3], Prosser [5], and Clark [6] defined privacy in general, Hayashi [4], Begum and Nausheen [7], and Jain et al. [8] considered privacy in terms of big data, and Pillitteri and Brewer [9] specifically focused on the context of smart grids. Pillitteri and Brewer [9] translated the privacy classifications defined by Clark [6] in the context of the smart grid application as shown in Table 1.This literature review follows the definition of privacy depicted by Pillitteri and Brewer [9].

Conclusions

     This article reviewed previous literature to provide building researchers with the basics of differential privacy implementation around data collected in buildings. Although the specific scope of this article is still in an early research stage, fast-growing data collection in buildings along with the breadth and scale of data available necessitated an examination of privacy preserving research for differential privacy. Because the topic is also not common in the representative building research journals, this study aimed to provide a greater level of detail for readers who might never have heard of differential privacy. To provide relevant context around privacy preservation in data, this literature review presented 1) privacy risks associated with data collected in buildings, 2) use cases that could be supported by analyses from these data, and 3) reviews of differential privacy implementation. The findings from the literature emphasize not only technical development but also engagement from stakeholders and policymakers in properly configuring differential privacy and protecting underlying privacy in data collected in buildings.