Analyzing Continuous Ks-Anonymization for Smart Meter Data

Research output: Contribution to book/Conference proceedings/Anthology/ReportConference contributionContributedpeer-review

Contributors

Abstract

Data anonymization is crucial to allow the widespread adoption of some technologies, such as smart meters. However, anonymization techniques should be evaluated in the context of a dataset to make meaningful statements about their eligibility for a particular use case. In this paper, we therefore analyze the suitability of continuous ks-anonymization with CASTLE for data streams generated by smart meters. We compare CASTLE ’s continuous, piecewise ks-anonymization with a global process in which all data is known at once, based on metrics like information loss and properties of the sensitive attribute. Our results suggest that continuous ks-anonymization of smart meter data is reasonable and ensures privacy while having comparably low utility loss.

Details

Original languageEnglish
Title of host publicationComputer Security. ESORICS 2023 International Workshops - CyberICS, DPM, CBT, and SECPRE, 2023, Revised Selected Papers
EditorsSokratis Katsikas, Frédéric Cuppens, Nora Cuppens-Boulahia, Costas Lambrinoudakis, Joaquin Garcia-Alfaro, Guillermo Navarro-Arribas, Pantaleone Nespoli, Christos Kalloniatis, John Mylopoulos, Annie Antón, Stefanos Gritzalis
Pages272-282
Number of pages11
ISBN (electronic)978-3-031-54204-6
Publication statusPublished - Sept 2023
Peer-reviewedYes

Publication series

SeriesLecture Notes in Computer Science
Volume14398
ISSN0302-9743

External IDs

Scopus 85187776866

Keywords