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Verlagsausgabe
DOI: 10.5445/IR/1000086897
Veröffentlicht am 24.10.2018
Originalveröffentlichung
DOI: 10.1186/s42162-018-0025-3

The influence of differential privacy on short term electric load forecasting

Eibl, Günther; Bao, Kaibin; Grassal, Philip-William; Bernau, Daniel; Schmeck, Hartmut

Abstract (englisch):
There has been a large number of contributions on privacy-preserving smart metering with Differential Privacy, addressing questions from actual enforcement at the smart meter to billing at the energy provider. However, exploitation is mostly limited to application of cryptographic security means between smart meters and energy providers. We illustrate along the use case of privacy preserving load forecasting that Differential Privacy is indeed a valuable addition that unlocks novel information flows for optimization. We show that (i) there are large differences in utility along three selected forecasting methods, (ii) energy providers can enjoy good utility especially under the linear regression benchmark model, and (iii) households can participate in privacy preserving load forecasting with an individual membership inference risk <60%, only 10% over random guessing.


Zugehörige Institution(en) am KIT Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB)
Kompetenzzentrum für angewandte Sicherheitstechnologie (KASTEL)
Publikationstyp Zeitschriftenaufsatz
Jahr 2018
Sprache Englisch
Identifikator ISSN: 2520-8942
URN: urn:nbn:de:swb:90-868974
KITopen ID: 1000086897
Erschienen in Energy Informatics
Band S1
Heft 48
Seiten 93-113
Bemerkung zur Veröffentlichung 7th DACH+ Conference on Energy Informatics, Oldenburg, Germany,11-12 October 2018
Vorab online veröffentlicht am 10.10.2018
Schlagworte Differential privacy; Load forecasting; Smart metering
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