Book contents
- Frontmatter
- Contents
- List of Contributors
- Preface
- Part I Statistical Learning
- Part II Data-Driven Anomaly Detection
- Part III Data Quality, Integrity, and Privacy
- 9 Data-Injection Attacks
- 10 Smart Meter Data Privacy
- 11 Data Quality and Privacy Enhancement
- Part IV Signal Processing
- Part V Large-Scale Optimization
- Part VI Game Theory
- Index
10 - Smart Meter Data Privacy
from Part III - Data Quality, Integrity, and Privacy
Published online by Cambridge University Press: 22 March 2021
- Frontmatter
- Contents
- List of Contributors
- Preface
- Part I Statistical Learning
- Part II Data-Driven Anomaly Detection
- Part III Data Quality, Integrity, and Privacy
- 9 Data-Injection Attacks
- 10 Smart Meter Data Privacy
- 11 Data Quality and Privacy Enhancement
- Part IV Signal Processing
- Part V Large-Scale Optimization
- Part VI Game Theory
- Index
Summary
Smart grids (SGs) promise to deliver dramatic improvements compared to traditional power grids thanks primarily to the large amount of data being exchanged and processed within the grid, which enables the grid to be monitored more accurately and at a much faster pace. The smart meter (SM) is one of the key devices that enable the SG concept by monitoring a household’s electricity consumption and reporting it to the utility provider (UP), i.e., the entity that sells energy to customers, or to the distribution system operator (DSO), i.e., the entity that operates and manages the grid. However, the very availability of rich and high-frequency household electricity consumption data, which enables a very efficient power grid management, also opens up unprecedented challenges on data security and privacy. To counter these threats, it is necessary to develop techniques that keep SM data private, and, for this reason, SM privacy has become a very active research area. The aim of this chapter is to provide an overview of the most significant privacy-preserving techniques for SM data, highlighting their main benefits and disadvantages.
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- Advanced Data Analytics for Power Systems , pp. 230 - 260Publisher: Cambridge University PressPrint publication year: 2021
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