Anonymization method based on sparse coding for power usage data

Keiya Haradat, Yuta Ohnot, Yuichi Nakamura, Hiroaki Nishit

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In recent years, there have been rapid increases in the number of network-connected devices such as computers, smartphones, and Internet of Things devices. Thus, large amounts of data have been accumulated such as locational data, website search histories, and power usage data. These data are used in various types of services. However, these data cannot be used easily for secondary purposes in some countries because of privacy problems. Therefore, privacy protection is necessary to apply these data in secondary uses where data anonymization is the usual solution. Many conventional methods are used for anonymizing power usage data, but the conventional method has three problems. First, it cannot anonymize time-series data. Second, the information loss is so large in the conventional method that the anonymized data are no longer suitable for secondary uses. Third, the conventional method cannot preserve the type of electrical appliance used. In this study, we propose a method for anonymizing power demand data, where sparse coding is used to solve the three problems that affect the conventional method. The proposed method can anonymize time series-data and it allows data to be analyzed at a chosen time. The proposed method was used to anonymize power usage data from the Urban Design Center Misono (UDCMi) and the experimental error rate decreased compared with the conventional method. The dictionary produced using the proposed method represents the electrical appliance data.

Original languageEnglish
Title of host publicationProceedings - IEEE 16th International Conference on Industrial Informatics, INDIN 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages571-576
Number of pages6
ISBN (Electronic)9781538648292
DOIs
Publication statusPublished - 2018 Sep 24
Externally publishedYes
Event16th IEEE International Conference on Industrial Informatics, INDIN 2018 - Porto, Portugal
Duration: 2018 Jul 182018 Jul 20

Publication series

NameProceedings - IEEE 16th International Conference on Industrial Informatics, INDIN 2018

Conference

Conference16th IEEE International Conference on Industrial Informatics, INDIN 2018
CountryPortugal
CityPorto
Period18/7/1818/7/20

Keywords

  • Anonymization
  • Dictionary
  • Error rate
  • Information loss
  • K-anonymity
  • Mondnan-clustering
  • Sparse coding

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems and Management
  • Industrial and Manufacturing Engineering

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  • Cite this

    Haradat, K., Ohnot, Y., Nakamura, Y., & Nishit, H. (2018). Anonymization method based on sparse coding for power usage data. In Proceedings - IEEE 16th International Conference on Industrial Informatics, INDIN 2018 (pp. 571-576). [8471982] (Proceedings - IEEE 16th International Conference on Industrial Informatics, INDIN 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/INDIN.2018.8471982