Search: onr:"swepub:oai:DiVA.org:umu-199472" >
DFTMicroagg: a dual...
DFTMicroagg: a dual-level anonymization algorithm for smart grid data
-
- Adewole, Kayode S. (author)
- Umeå universitet,Institutionen för datavetenskap,Department of Computer Science, University of Ilorin, Ilorin, Nigeria
-
- Torra, Vicenç (author)
- Umeå universitet,Institutionen för datavetenskap
-
(creator_code:org_t)
- 2022-09-07
- 2022
- English.
-
In: International Journal of Information Security. - : Springer. - 1615-5262 .- 1615-5270. ; 21, s. 1299-1321
- Related links:
-
https://doi.org/10.1...
-
show more...
-
https://umu.diva-por... (primary) (Raw object)
-
https://urn.kb.se/re...
-
https://doi.org/10.1...
-
show less...
Abstract
Subject headings
Close
- The introduction of advanced metering infrastructure (AMI) smart meters has given rise to fine-grained electricity usage data at different levels of time granularity. AMI collects high-frequency daily energy consumption data that enables utility companies and data aggregators to perform a rich set of grid operations such as demand response, grid monitoring, load forecasting and many more. However, the privacy concerns associated with daily energy consumption data has been raised. Existing studies on data anonymization for smart grid data focused on the direct application of perturbation algorithms, such as microaggregation, to protect the privacy of consumers. In this paper, we empirically show that reliance on microaggregation alone is not sufficient to protect smart grid data. Therefore, we propose DFTMicroagg algorithm that provides a dual level of perturbation to improve privacy. The algorithm leverages the benefits of discrete Fourier transform (DFT) and microaggregation to provide additional layer of protection. We evaluated our algorithm on two publicly available smart grid datasets with millions of smart meters readings. Experimental results based on clustering analysis using k-Means, classification via k-nearest neighbor (kNN) algorithm and mean hourly energy consumption forecast using Seasonal Auto-Regressive Integrated Moving Average with eXogenous (SARIMAX) factors model further proved the applicability of the proposed method. Our approach provides utility companies with more flexibility to control the level of protection for their published energy data.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Keyword
- Discrete Fourier transform
- Load forecasting
- Microaggregation
- Privacy preserving
- Smart grid
- Smart meters
Publication and Content Type
- ref (subject category)
- art (subject category)
Find in a library
To the university's database