Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
97 tokens/sec
GPT-4o
53 tokens/sec
Gemini 2.5 Pro Pro
44 tokens/sec
o3 Pro
5 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Privacy-preserving and Efficient Aggregation based on Blockchain for Power Grid Communications in Smart Communities (1806.01056v1)

Published 4 Jun 2018 in cs.CR

Abstract: Intelligence is one of the most important aspects in the development of our future communities. Ranging from smart home, smart building, to smart city, all these smart infrastructures must be supported by intelligent power supply. Smart grid is proposed to solve all challenges of future electricity supply. In smart grid, in order to realize optimal scheduling, a Smart Meter (SM) is installed at each home to collect the near real-time electricity consumption data, which can be used by the utilities to offer better smart home services. However, the near real-time data may disclose user's privacy. An adversary may track the application usage patterns by analyzing the user's electricity consumption profile. In this paper, we propose a privacy-preserving and efficient data aggregation scheme. We divide users into different groups and each group has a private blockchain to record its members' data. To preserve the inner privacy within a group, we use pseudonym to hide user's identity, and each user may create multiple pseudonyms and associate his/her data with different pseudonyms. In addition, the bloom filter is adopted for fast authentication. The analysis shows that the proposed scheme can meet the security requirements, and achieve a better performance than other popular methods.

User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (7)
  1. Zhitao Guan (16 papers)
  2. Guanlin Si (2 papers)
  3. Xiaosong Zhang (29 papers)
  4. Longfei Wu (16 papers)
  5. Nadra Guizani (9 papers)
  6. Xiaojiang Du (94 papers)
  7. Yinglong Ma (9 papers)
Citations (300)

Summary

Privacy-Preserving and Efficient Aggregation Based on Blockchain for Power Grid Communications in Smart Communities

The paper discusses a novel approach to secure and efficient data aggregation in the context of smart grids within smart communities. It primarily focuses on protecting user privacy while ensuring the integrity and efficiency of electricity consumption data management through blockchain technology. The proposed method addresses critical challenges related to privacy leaks, authentication efficiency, and the lack of a trusted third-party for data aggregation.

Key Contributions

The authors have introduced a comprehensive scheme to protect user data privacy while enabling efficient aggregation of electricity consumption data via smart meters (SM). Central to the approach is the use of a private blockchain per user group to record and authenticate data contributions from group members.

  1. Privacy Preservation: User anonymity is achieved through pseudonyms. Every user can generate multiple pseudonyms, allowing them to mask their data origin. The reliance on pseudonyms, supported by zero-knowledge proofs, secures the user's real identity against potential de-anonymization.
  2. Efficient Authentication: The introduction of bloom filters enhances authentication speed and efficiency. Bloom filters enable quick validation of pseudonyms, avoiding the computational overhead traditionally associated with cryptographic identity verification.
  3. Blockchain Utilization: The use of blockchain technology ensures the integrity of data aggregated by randomly selected node within a user group. This decentralization mitigates the risk associated with a single point of failure or a potentially malicious trusted third party.
  4. Mining Node Selection: A unique strategy for mining node selection is proposed, based on the proximity of a user's data to the average group consumption. This random yet deterministic method guarantees fairness in mining duties.

Performance Evaluation

The paper provides a comparative analysis of its proposed scheme against existing methods like PPM-HDA and DG-APED. The authors demonstrate a reduction in computational cost and time complexity, particularly in the pseudonym authentication process, attributed to the efficient implementation of bloom filters.

Security Analysis

The scheme effectively addresses the challenge of preserving data privacy against internal and external threats. The use of pseudonyms and blockchain secures the communication and storage of sensitive data, while zero-knowledge proofs provide an additional layer of security, ensuring the authenticity of user contributions. While the paper acknowledges the intrinsic error probability of bloom filters, it posits that this margin is sufficiently minimized to be negligible by adjusting array sizes.

Implications and Future Work

From a theoretical perspective, this research advances the field's understanding of blockchain's applicability beyond monetary transactions. It introduces a scalable solution tailored for energy infrastructures, reflecting an innovative means by which blockchain can be employed in other data-sensitive, distributed systems beyond Power Grid Communications.

The paper speculatively hints at future endeavors aiming to minimize initialization computational overhead, perhaps integrating more agile cryptographic protocols or machine learning techniques for predictive node selection and data validation. Moreover, ongoing advancements in smart grid technologies and blockchain's integration into decentralized applications present opportunities for enhancement and iteration of this proposed scheme.

Conclusion

This paper makes significant strides in harmonizing privacy preservation and data aggregation efficiency using blockchain in smart grid environments. By reducing reliance on centralized trust and improving authentication processes, the approach presents a pragmatic solution to data transparency and privacy concerns inherent in smart community infrastructures.