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

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-enhancing distributed protocol for data aggregation based on blockchain and homomorphic encryption

The recent increase in reported incidents of security breaches compromising users' privacy call into question the current centralized model in which third-parties collect and control massive amounts of personal data. Blockchain has demonstrated that trusted and auditable computing is possible using a decentralized network of peers accompanied by a public ledger. Furthermore, Homomorphic Encryption (HE) guarantees confidentiality not only on the computation but also on the transmission, and storage processes. The synergy between Blockchain and HE is rapidly increasing in the computing environment. This research proposes a privacy-enhancing distributed and secure protocol for data aggregation backboned by Blockchain and HE technologies. Blockchain acts as a distributed ledger which facilitates efficient data aggregation through a Smart Contract. On the top, HE will be used for data encryption allowing private aggregation operations. The theoretical description, potential applications, a suggested implementation and a performance analysis are presented to validate the proposed solution.This work has been partially supported by the Basque Country Government under the ELKARTEK program, project TRUSTIND (KK- 2020/00054). It has also been partially supported by the H2020 TERMINET project (GA 957406)

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

Fault-Tolerant Secure Data Aggregation Schemes in Smart Grids: Techniques, Design Challenges, and Future Trends

Secure data aggregation is an important process that enables a smart meter to perform efficiently and accurately. However, the fault tolerance and privacy of the user data are the most serious concerns in this process. While the security issues of Smart Grids are extensively studied, these two issues have been ignored so far. Therefore, in this paper, we present a comprehensive survey of fault-tolerant and differential privacy schemes for the Smart Gird. We selected papers from 2010 to 2021 and studied the schemes that are specifically related to fault tolerance and differential privacy. We divided all existing schemes based on the security properties, performance evaluation, and security attacks. We provide a comparative analysis for each scheme based on the cryptographic approach used. One of the drawbacks of existing surveys on the Smart Grid is that they have not discussed fault tolerance and differential privacy as a major area and consider them only as a part of privacy preservation schemes. On the basis of our work, we identified further research areas that can be explored

Lightweight and privacy-friendly spatial data aggregation for secure power supply and demand management in smart grids

The concept of smart metering allows real-time measurement of power demand which in turn is expected to result in more efficient energy use and better load balancing. However, finely granular measurements reported by smart meters can lead to starkly increased exposure of sensitive information, including various personal attributes and activities. Even though several security solutions have been proposed in recent years to address this issue, most of the existing solutions are based on publickey cryptographic primitives such as homomorphic encryption, elliptic curve digital signature algorithms (ECDSA), etc. which are ill-suited for the resource constrained smart meters. On the other hand, to address the computational inefficiency issue, some masking-based solutions have been proposed. However, these schemes cannot ensure some of the imperative security properties such as consumer’s privacy, sender authentication, etc. In this paper, we first propose a lightweight and privacyfriendly masking-based spatial data aggregation scheme for secure forecasting of power demand in smart grids. Our scheme only uses lightweight cryptographic primitives such as hash functions, exclusive-OR operations, etc. Subsequently, we propose a secure billing solution for smart grids. As compared to existing solutions, our scheme is simple and can ensure better privacy protection and computational efficiency, which are essential for smart grids

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