A Lightweight Privacy-Preserved Spatial and Temporal Aggregation of Energy Data

Smart grid provides fine-grained real time energy consumption, and it is able to improve the efficiency of energy management. It enables the collection of energy consumption data from consumer and hence has raised serious privacy concerns. Energy consumption data, a form of personal information that reveals behavioral patterns can be used to identify electrical appliances being used by the user through the electricity load signature, thus making it possible to further reveal the residency pattern of a consumer’s household or appliances usage habit. This paper proposes to enhance the privacy of energy con- sumption data by enabling the utility to retrieve the aggregated spatial and temporal consumption without revealing individual energy consumption. We use a lightweight cryptographic mech- anism to mask the energy consumption data by adding random noises to each energy reading and use Paillier’s additive homo- morphic encryption to protect the noises. When summing up the masked energy consumption data for both Spatial and Temporal aggregation, the noises cancel out each other, hence resulting in either the total sum of energy consumed in a neighbourhood at a particular time, or the total sum of energy consumed by a household in a day. No third party is able to derive the energy consumption pattern of a household in real time. A proof-of- concept was implemented to demonstrate the feasibility of the system, and the results show that the system can be efficiently deployed on a low-cost computing platform

Key Management Systems for Smart Grid Advanced Metering Infrastructure: A Survey

Smart Grids are evolving as the next generation power systems that involve changes in the traditional ways of generation, transmission and distribution of power. Advanced Metering Infrastructure (AMI) is one of the key components in smart grids. An AMI comprises of systems and networks, that collects and analyzes data received from smart meters. In addition, AMI also provides intelligent management of various power-related applications and services based on the data collected from smart meters. Thus, AMI plays a significant role in the smooth functioning of smart grids. AMI is a privileged target for security attacks as it is made up of systems that are highly vulnerable to such attacks. Providing security to AMI is necessary as adversaries can cause potential damage against infrastructures and privacy in smart grid. One of the most effective and challenging topic's identified, is the Key Management System (KMS), for sustaining the security concerns in AMI. Therefore, KMS seeks to be a promising research area for future development of AMI. This survey work highlights the key security issues of advanced metering infrastructures and focuses on how key management techniques can be utilized for safeguarding AMI. First of all, we explore the main features of advanced metering infrastructures and identify the relationship between smart grid and AMI. Then, we introduce the security issues and challenges of AMI. We also provide a classification of the existing works in literature that deal with secure key management system in AMI. Finally, we identify possible future research directions of KMS in AMI

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