Techniques, Taxonomy, and Challenges of Privacy Protection in the Smart Grid

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ABSTRACT

As the ease with which any data are collected and transmitted increases, more privacy concerns arise leading to an increasing need to protect and preserve it. Much of the recent high-profile coverage of data mishandling and public mis-leadings about various aspects of privacy exasperates the severity. The Smart Grid (SG) is no exception with its key characteristics aimed at supporting bi-directional information flow between the consumer of electricity and the utility provider.

What makes the SG privacy even more challenging and intriguing is the fact that the very success of the initiative depends on the expanded data generation, sharing, and processing. In particular, the deployment of smart meters whereby energy consumption information can easily be collected leads to major public hesitations about the technology.

Thus, to successfully transition from the traditional Power Grid to the SG of the future, public concerns about their privacy must be explicitly addressed and fears must be allayed. Along these lines, this chapter introduces some of the privacy issues and problems in the domain of the SG, develops a unique taxonomy of some of the recently proposed privacy protecting solutions as well as some if the future privacy challenges that must be addressed in the future.

BACKGROUND ON SMART GRID

Fig. 15.3 NIST’s 7-Domain Smart Grid Conceptual Model

Fig. 15.3 NIST’s 7-Domain Smart Grid Conceptual Model

The most relevant domain of the NIST Conceptual Model for this chapter is the Distribution Domain (as depicted in Fig. 15.3), because it is the main physical interface between the end-user and the SG and it is the center of almost all of the potential privacy violations. Note that it is also the Distribution Domain that is responsible for achieving the most widely-cited benefits of the SG which include control, measurement, sensing, data collection and storage, and optimization of operations that take place in or for it.

SMART GRID PRIVACY ISSUES

Fig. 15.10 Aggregate power consumption data

Fig. 15.10 Aggregate power consumption data

An illustration of the concept is presented in as depicted in Figs. 15.10, 15.11, and 15.12, where a behavior extraction algorithm implemented in Matlab is used. DSM and Demand response systems provide sufficient power usage information to reveal in-home activities that might be disturbing for the privacy of the households.

PRIVACY SOLUTIONS

Fig. 15.13 Privacy-preservation techniques used in the SG

Fig. 15.13 Privacy-preservation techniques used in the SG

A comprehensive and novel taxonomy of the SG privacy-protection mechanisms and approaches is given in Fig. 15.13. We divide the SG approaches into spatial and temporal broad categories. The former include those that devise privacy into the system by means of a physical device or entity while the latter incorporates privacy into the system by means of logical extensions. We note that the individual categories identified in Fig. 15.13 do not necessarily indicate an exclusive technique. In fact, a privacy preservation proposal reported in the literature may, and usually does, implement a combination of them.

CHALLENGES AND OPPORTUNITIES

The preservation of privacy in the SG environment has many fundamental open challenges that still need to be solved. As our literature survey shows, several research projects have been investigating privacy-preserving techniques for the SG environment in the last few years. We found that there is need for privacy to be comprehensively regulated through legal and regulatory frameworks for enhancing users confidence and for reinforcing individual’s privacy rights.

CONCLUSION

Over the past several years we have witnessed huge investments and interests from industry and governments in SG technologies. Various stakeholders (residential/ commercial customers, local government, utility operators, etc.) are expected to reap several benefits associated with the SG including improved energy efficiency, increased reliability, reduced energy costs, greater flexibility in energy consumption, better safety and security, and an improved environment (through renewable, renewable non-variable, non-renewable/non-variable energy sources).

The deployment of SG technologies has also raised considerable concerns in data privacy issues of SG users, as we have discussed in this chapter. The privacy concerns are mostly related to the collection and use of energy consumption data. In this context, we have discussed various SG privacy issues and we have presented SG privacy architectures and approaches that have been recently proposed in the literature.

A unique taxonomy of the various privacy protection mechanisms proposed in the literature has been developed. We also identified the various strengths and weaknesses of these privacy solutions. The success of SG technology and its wide acceptance rely on gaining the trust and confidence of customers, which in turn depends on assurances regarding the protection of their privacy.

Source: The University of Michigan
Authors: Suleyman Uludag | Sherali Zeadally | Mohamad Badra

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