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

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 pro- cessing. In particular, the deployment of smart meters whereby energy consumption information can easily be collected leads to major public hesitations about the tech- nology. 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.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/111644/1/Uludag2015SG-privacy_book-chapter.pd

Privacy-preserving power usage control in smart grids

The smart grid (SG) has been emerging as the next-generation intelligent power grid system because of its ability to efficiently monitor, predicate, and control energy generation, transmission, and consumption by analyzing users\u27 real-time electricity information. Consider a situation in which the utility company would like to smartly protect against a power outage. To do so, the company can determine a threshold for a neighborhood. Whenever the total power usage from the neighborhood exceeds the threshold, some or all of the households need to reduce their energy consumption to avoid the possibility of a power outage. This problem is referred to as threshold-based power usage control (TPUC) in the literature. In order to solve the TPUC problem, the utility company is required to periodically collect the power usage data of households. However, it has been well documented that these power usage data can reveal consumers\u27 daily activities and violate personal privacy. To avoid the privacy concerns, privacy-preserving power usage control (P-PUC) protocols are proposed under two strategies: adjustment based on maximum power usage and adjustment based on individual power usage. These protocols allow a utility company to manage power consumption effectively and at the same time, preserve the privacy of all involved parties. Furthermore, the practical value of the proposed protocols is empirically shown through various experiments --Abstract, page iii

Privacy-Preserving Data Falsification Detection in Smart Grids using Elliptic Curve Cryptography and Homomorphic Encryption

In an advanced metering infrastructure (AMI), the electric utility collects power consumption data from smart meters to improve energy optimization and provides detailed information on power consumption to electric utility customers. However, AMI is vulnerable to data falsification attacks, which organized adversaries can launch. Such attacks can be detected by analyzing customers\u27 fine-grained power consumption data; however, analyzing customers\u27 private data violates the customers\u27 privacy. Although homomorphic encryption-based schemes have been proposed to tackle the problem, the disadvantage is a long execution time. This paper proposes a new privacy-preserving data falsification detection scheme to shorten the execution time. We adopt elliptic curve cryptography (ECC) based on homomorphic encryption (HE) without revealing customer power consumption data. HE is a form of encryption that permits users to perform computations on the encrypted data without decryption. Through ECC, we can achieve light computation. Our experimental evaluation showed that our proposed scheme successfully achieved 18 times faster than the CKKS scheme, a common HE scheme