An adaptive privacy protection framework for user energy data using dictionary learning and watermarking techniques

Haiwen Chen, Wei Guo, Kaiqi Sun, Xuan Wang, Shouxiang Wang and Luyang Guo

Applied Energy, 2024, vol. 370, issue C, No S0306261924009280

Abstract: With the rise of user energy consumption data as a significant data asset, data privacy has emerged as a critical concern. To address users' diverse attitudes towards data sharing and their varied usage requirements, this paper introduces an adaptive privacy protection framework for user energy data based on dictionary learning and watermarking techniques. Central to this framework is an innovative digital watermark anonymization method designed to meet the dual objectives of encryption and anonymous data sharing. This method employs sparse dictionary decomposition to embed confidential user information within sparse coefficients, significantly enhancing computational efficiency while minimally impacting the integrity of the original data. Additionally, through sparse data representation, the framework achieves effective data compression, addressing the challenges of extensive storage requirements inherent in maintaining original energy consumption data, encryption results, and the data sharing process. Security analysis and case studies demonstrate the proposed method's robustness against eavesdropping, unauthorized access, and other security threats.

Keywords: Smart meter; Energy consumption data; Privacy protection; Cloud storage (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261924009280
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:370:y:2024:i:c:s0306261924009280

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2024.123545

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().