Precision variable anonymization method supporting transprecision computing

Keiya Harada, Henri Pierre Charles, Hiroaki Nishi

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

Abstract

Recently, the number of Internet of Things (IoT) sensors has been increasing rapidly; hence, various data are gathered. As a secondary use of the data, they are useful in providing new services, such as the demand response service in the Smart Grid. However, data services cause several problems in preserving privacy and during computation. This study focuses on these two significant problems. First, the invasion of privacy while using the data to provide such new services is problematic. A lot of private information is available in the data. For example, power consumption data may reveal the lifestyle of the residents, and the technique of obtaining information is known as nonintrusive load monitoring. Second, the penetration of IoT devices and sensors increases the computational and communicating energy consumption for processing the data and for providing various services using the data. In this paper, a new method is proposed to solve these two problems. This method is based on the fact that the anonymization process reduces the amount of information itself, as well as the quantity of computational resources required. This leads to a trade-off between anonymization level and computational cost. For example, raw data have a maximum amount of information and maximum computational cost. In contrast, fully generalized data (all zero data) have minimum amount of information and minimum computational cost. Compared to the conventional method, the proposed method demonstrated lower precision and a higher error rate. Therefore, the proposed method aims to control the trade-off and enables the provision of anonymized data with less information, the required anonymity level, and low computational cost compared to the conventional method. The proposed method is practiced using power consumption data gathered from the Urban Design Center Misiono (UDCMi) and the demand response service is evaluated as an experiment using the data. In this evaluation, a simple model of energy consumption was used in the calculation, which uses the required bit width of the arithmetic logic unit (ALU) for providing the service. The computational efficiency of the proposed method was increased by 60% when k = 2 and by 65% when k = 3, 4, 5, 6 compared to the conventional method. The method can also maintain an acceptable range of service error. The transprecision platform can restrict energy consumption by reducing the bit width of the data. Therefore, the proposed anonymization method can also reduce energy consumption by achieving lower usage of the ALU based on the transprecision architecture.

Original languageEnglish
Title of host publication22nd International Conference on Advanced Communications Technology
Subtitle of host publicationDigital Security Global Agenda for Safe Society, ICACT 2020 - Proceeding
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages35-42
Number of pages8
ISBN (Electronic)9791188428045
DOIs
Publication statusPublished - 2020 Feb
Event22nd International Conference on Advanced Communications Technology, ICACT 2020 - Pyeongchang, Korea, Republic of
Duration: 2020 Feb 162020 Feb 19

Publication series

NameInternational Conference on Advanced Communication Technology, ICACT
Volume2020
ISSN (Print)1738-9445

Conference

Conference22nd International Conference on Advanced Communications Technology, ICACT 2020
CountryKorea, Republic of
CityPyeongchang
Period20/2/1620/2/19

Keywords

  • anonymization
  • approximate computing
  • k-anonymity
  • power consumption data
  • transprecision computing

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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