Privacy-Preserving Market for Smart Grid Data Trading

2020 ◽  
Author(s):  
Ritesh Mohan Acharya ◽  
Manickavasagam Parvathy Selvan
Keyword(s):  
Smart Grid ◽  
Privacy Preserving ◽  
Grid Data

scholarly journals Decentralized Privacy-Preserving Data Aggregation Scheme for Smart Grid Based on Blockchain

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5282 ◽  
Author(s):  
Hongbin Fan ◽  
Yining Liu ◽  
Zhixin Zeng
Keyword(s):  
Smart Grid ◽  
Data Aggregation ◽  
Privacy Preserving ◽  
Third Party ◽  
Security And Privacy ◽  
Communication Overhead ◽  
User Privacy ◽  
Grid Data ◽  
Trusted Third Party ◽  
Grid Based

As a next-generation power system, the smart grid can implement fine-grained smart metering data collection to optimize energy utilization. Smart meters face serious security challenges, such as a trusted third party or a trusted authority being attacked, which leads to the disclosure of user privacy. Blockchain provides a viable solution that can use its key technologies to solve this problem. Blockchain is a new type of decentralized protocol that does not require a trusted third party or a central authority. Therefore, this paper proposes a decentralized privacy-preserving data aggregation (DPPDA) scheme for smart grid based on blockchain. In this scheme, the leader election algorithm is used to select a smart meter in the residential area as a mining node to build a block. The node adopts Paillier cryptosystem algorithm to aggregate the user’s power consumption data. Boneh-Lynn-Shacham short signature and SHA-256 function are applied to ensure the confidentiality and integrity of user data, which is convenient for billing and power regulation. The scheme protects user privacy data while achieving decentralization, without relying on TTP or CA. Security analysis shows that our scheme meets the security and privacy requirements of smart grid data aggregation. The experimental results show that this scheme is more efficient than existing competing schemes in terms of computation and communication overhead.

scholarly journals Privacy-Preserving Multidimensional Data Aggregation Scheme for Smart Grid

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Yousheng Zhou ◽  
Xinyun Chen ◽  
Meihuan Chen
Keyword(s):  
Smart Grid ◽  
Data Aggregation ◽  
Data Privacy ◽  
Random Oracle Model ◽  
Privacy Preserving ◽  
Multidimensional Data ◽  
Communication Overhead ◽  
Security Threat ◽  
Grid Data ◽  
Aggregation Scheme

In a smart grid, data aggregation is a common method to evaluate regional power consumption. Data leakage in the process of data transmission poses a security threat to the privacy of users. Many existing data aggregation schemes can only aggregate one-dimensional data; however, it is necessary to aggregate multidimensional data in practical smart grid applications. Therefore, this paper proposes a privacy-preserving multidimensional data aggregation scheme, which can aggregate multidimensional data and protect the individual user’s identity and data privacy. The security of the proposed scheme is proved under the random oracle model. The simulation results show that the proposed scheme has great advantages in computing overhead, and the communication overhead also meets the requirements of the smart grid.

Privacy-Preserving Homomorphic Masking for Smart Grid Data Analytics in the Cloud

2020 ◽  
pp. 265-292
Author(s):  
Pirathayini Srikantha ◽  
Anna Shi ◽  
Bokun Zhang ◽  
Deepa Kundur
Keyword(s):  
Smart Grid ◽  
Data Analytics ◽  
Privacy Preserving ◽  
Grid Data

PARK: A privacy-preserving aggregation scheme with adaptive key management for smart grid

2013 ◽  
Author(s):  
Kuan Zhang ◽  
Rongxing Lu ◽  
Xiaohui Liang ◽  
Jian Qiao ◽  
Xuemin Sherman Shen
Keyword(s):  
Smart Grid ◽  
Key Management ◽  
Privacy Preserving ◽  
Aggregation Scheme

scholarly journals The IEC 61850 Sampled Measured Values Protocol: Analysis, Threat Identification, and Feasibility of Using NN Forecasters to Detect Spoofed Packets

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3731 ◽  
Author(s):  
Mohamad El Hariri ◽  
Eric Harmon ◽  
Tarek Youssef ◽  
Mahmoud Saleh ◽  
Hany Habib ◽  
...  
Keyword(s):  
Smart Grid ◽  
Protocol Analysis ◽  
Data Detection ◽  
Detection Accuracy ◽  
Wide Spread ◽  
Grid Data ◽  
Iec 61850 ◽  
Forecasting Error ◽  
Heterogeneous Nature ◽  
Statistical Indicators

The operation of the smart grid is anticipated to rely profoundly on distributed microprocessor-based control. Therefore, interoperability standards are needed to address the heterogeneous nature of the smart grid data. Since the IEC 61850 emerged as a wide-spread interoperability standard widely accepted by the industry, the Sampled Measured Values method has been used to communicate digitized voltage and current measurements. Realizing that current and voltage measurements (i.e., feedback measurements) are necessary for reliable and secure noperation of the power grid, firstly, this manuscript provides a detailed analysis of the Sampled Measured Values protocol emphasizing its advantages, then, it identifies vulnerabilities in this protocol and explains the cyber threats associated to these vulnerabilities. Secondly, current efforts to mitigate these vulnerabilities are outlined and the feasibility of using neural network forecasters to detect spoofed sampled values is investigated. It was shown that although such forecasters have high spoofed data detection accuracy, they are prone to the accumulation of forecasting error. Accordingly, this paper also proposes an algorithm to detect the accumulation of the forecasting error based on lightweight statistical indicators. The effectiveness of the proposed methods is experimentally verified in a laboratory-scale smart grid testbed.

Sign in / Sign up

Export Citation Format

Share Document