Influence of data granularity on nonintrusive appliance load monitoring

G. Eibl; D. Engel

doi:10.1145/2600918.2600920

Influence of data granularity on nonintrusive appliance load monitoring

G. Eibl
, D. Engel

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Decreasing time resolution is the simplest possible privacy enhancing technique for energy consumption data. However, its impact on privacy analyses of load signals has never been studied systematically. Non-intrusive appliance load monitoring algorithms (NIALM) have originally been designed for energy disaggregation for subsequent energy feedback. However, the information on appliance use may also be misused for the extraction of personal information. In this work, the effect of decreasing the time resolution in the usual first step, namely edge detection, is studied. It is shown that event values can be estimated rather reliably, but the detection rate of events significantly decreases with increasing measurement time interval. Copyright 2014 ACM.

Original language	English
Title of host publication	IH&MMSec '14: Proceedings of the 2nd ACM workshop on Information hiding and multimedia security
Publisher	Association for Computing Machinery
Pages	147-151
Number of pages	5
ISBN (Print)	978-1-4503-2647-6
DOIs	https://doi.org/10.1145/2600918.2600920
Publication status	Published - 2014
Event	2nd ACM Workshop on Information Hiding and Multimedia Security, IH and MMSec 2014 - Salzburg, Austria Duration: 11 Jun 2014 → 13 Jun 2014 http://www.sigmm.org/archive/IHMMSec/ihmmsec14/www.ihmmsec.org/index.php/ihmmsec2014.html

Workshop

Workshop	2nd ACM Workshop on Information Hiding and Multimedia Security, IH and MMSec 2014
Abbreviated title	IH and MMSec 2014
Country/Territory	Austria
City	Salzburg
Period	11/06/14 → 13/06/14
Internet address	http://www.sigmm.org/archive/IHMMSec/ihmmsec14/www.ihmmsec.org/index.php/ihmmsec2014.html

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Data representation
Edge detection
Load disaggregation
Privacy enhancement
Smart metering
Data privacy
Energy utilization
Signal analysis
Data representations
Disaggregation
Energy consumption datum
Measurement time intervals
Non-intrusive appliance load monitoring
Personal information
Privacy-enhancing techniques
Electric load management

Access to Document

10.1145/2600918.2600920

https://www.scopus.com/inward/record.uri?eid=2-s2.0-84922424616&doi=10.1145%2f2600918.2600920&partnerID=40&md5=78f5a422955629a6b0653ce0ce267cbb

Cite this

@inproceedings{8fecc45ae1a94225b982efdea40bce7d,

title = "Influence of data granularity on nonintrusive appliance load monitoring",

abstract = "Decreasing time resolution is the simplest possible privacy enhancing technique for energy consumption data. However, its impact on privacy analyses of load signals has never been studied systematically. Non-intrusive appliance load monitoring algorithms (NIALM) have originally been designed for energy disaggregation for subsequent energy feedback. However, the information on appliance use may also be misused for the extraction of personal information. In this work, the effect of decreasing the time resolution in the usual first step, namely edge detection, is studied. It is shown that event values can be estimated rather reliably, but the detection rate of events significantly decreases with increasing measurement time interval. Copyright 2014 ACM.",

keywords = "Data representation, Edge detection, Load disaggregation, Privacy enhancement, Smart metering, Data privacy, Energy utilization, Signal analysis, Data representations, Disaggregation, Energy consumption datum, Measurement time intervals, Non-intrusive appliance load monitoring, Personal information, Privacy-enhancing techniques, Electric load management",

author = "G. Eibl and D. Engel",

note = "Conference code: 113417 Cited By :12 Export Date: 14 December 2023; 2nd ACM Workshop on Information Hiding and Multimedia Security, IH and MMSec 2014, IH and MMSec 2014 ; Conference date: 11-06-2014 Through 13-06-2014",

year = "2014",

doi = "10.1145/2600918.2600920",

language = "English",

isbn = "978-1-4503-2647-6",

pages = "147--151",

booktitle = "IH\&MMSec '14: Proceedings of the 2nd ACM workshop on Information hiding and multimedia security",

publisher = "Association for Computing Machinery",

address = "United States",

url = "http://www.sigmm.org/archive/IHMMSec/ihmmsec14/www.ihmmsec.org/index.php/ihmmsec2014.html",

}

Eibl, G & Engel, D 2014, Influence of data granularity on nonintrusive appliance load monitoring. in IH&MMSec '14: Proceedings of the 2nd ACM workshop on Information hiding and multimedia security. Association for Computing Machinery, pp. 147-151, 2nd ACM Workshop on Information Hiding and Multimedia Security, IH and MMSec 2014, Salzburg, Austria, 11/06/14. https://doi.org/10.1145/2600918.2600920

TY - GEN

T1 - Influence of data granularity on nonintrusive appliance load monitoring

AU - Eibl, G.

AU - Engel, D.

N1 - Conference code: 113417 Cited By :12 Export Date: 14 December 2023

PY - 2014

Y1 - 2014

N2 - Decreasing time resolution is the simplest possible privacy enhancing technique for energy consumption data. However, its impact on privacy analyses of load signals has never been studied systematically. Non-intrusive appliance load monitoring algorithms (NIALM) have originally been designed for energy disaggregation for subsequent energy feedback. However, the information on appliance use may also be misused for the extraction of personal information. In this work, the effect of decreasing the time resolution in the usual first step, namely edge detection, is studied. It is shown that event values can be estimated rather reliably, but the detection rate of events significantly decreases with increasing measurement time interval. Copyright 2014 ACM.

AB - Decreasing time resolution is the simplest possible privacy enhancing technique for energy consumption data. However, its impact on privacy analyses of load signals has never been studied systematically. Non-intrusive appliance load monitoring algorithms (NIALM) have originally been designed for energy disaggregation for subsequent energy feedback. However, the information on appliance use may also be misused for the extraction of personal information. In this work, the effect of decreasing the time resolution in the usual first step, namely edge detection, is studied. It is shown that event values can be estimated rather reliably, but the detection rate of events significantly decreases with increasing measurement time interval. Copyright 2014 ACM.

KW - Data representation

KW - Edge detection

KW - Load disaggregation

KW - Privacy enhancement

KW - Smart metering

KW - Data privacy

KW - Energy utilization

KW - Signal analysis

KW - Data representations

KW - Disaggregation

KW - Energy consumption datum

KW - Measurement time intervals

KW - Non-intrusive appliance load monitoring

KW - Personal information

KW - Privacy-enhancing techniques

KW - Electric load management

U2 - 10.1145/2600918.2600920

DO - 10.1145/2600918.2600920

M3 - Conference contribution

SN - 978-1-4503-2647-6

SP - 147

EP - 151

BT - IH&MMSec '14: Proceedings of the 2nd ACM workshop on Information hiding and multimedia security

PB - Association for Computing Machinery

T2 - 2nd ACM Workshop on Information Hiding and Multimedia Security, IH and MMSec 2014

Y2 - 11 June 2014 through 13 June 2014

ER -

Influence of data granularity on nonintrusive appliance load monitoring

Abstract

Workshop

UN SDGs

Keywords

Access to Document

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Cite this