Smart grid data anonymization for smart grid privacy

Shahidul Hoque; Aneel Rahim; Francesco Di Cerbo

doi:10.1007/978-3-319-12574-9_8

Smart grid data anonymization for smart grid privacy

Shahidul Hoque
, Aneel Rahim
, Francesco Di Cerbo

Research output: Contribution to journal › Article › peer-review

Abstract

We present an approach to adopt the DB Anonymizer (Database Anonymizer) GE (Generic Enabler) in the context of a case study relating to a Smart Grid Charging Optimization System (COS) that has been developed using real time Electric Vehicle (EV) and Wind energy data. The paper takes consideration of DB Anonymizer GE software for data anonymization with Smart Grid data use case and without Smart Grid data. In addition, the implementation of EV data anonymization and robustness of its anonymization strategy set is evaluated and described in the paper, along with the lessons learned and the potential for future improvements to the data anonymization strategy determination. The novelty of the mechanism itself stems from the effective evaluation of the GE for Smart Grid environment and hence, enhances the privacy preservation capabilities of the Charging Optimization System.

Original language	English
Pages (from-to)	89-96
Number of pages	8
Journal	Communications in Computer and Information Science
Volume	470
DOIs	https://doi.org/10.1007/978-3-319-12574-9_8
Publication status	Published - 2014
Externally published	Yes

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10.1007/978-3-319-12574-9_8

Cite this

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title = "Smart grid data anonymization for smart grid privacy",

abstract = "We present an approach to adopt the DB Anonymizer (Database Anonymizer) GE (Generic Enabler) in the context of a case study relating to a Smart Grid Charging Optimization System (COS) that has been developed using real time Electric Vehicle (EV) and Wind energy data. The paper takes consideration of DB Anonymizer GE software for data anonymization with Smart Grid data use case and without Smart Grid data. In addition, the implementation of EV data anonymization and robustness of its anonymization strategy set is evaluated and described in the paper, along with the lessons learned and the potential for future improvements to the data anonymization strategy determination. The novelty of the mechanism itself stems from the effective evaluation of the GE for Smart Grid environment and hence, enhances the privacy preservation capabilities of the Charging Optimization System.",

author = "Shahidul Hoque and Aneel Rahim and \{Di Cerbo\}, Francesco",

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AU - Hoque, Shahidul

AU - Rahim, Aneel

AU - Di Cerbo, Francesco

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2014.

PY - 2014

Y1 - 2014

N2 - We present an approach to adopt the DB Anonymizer (Database Anonymizer) GE (Generic Enabler) in the context of a case study relating to a Smart Grid Charging Optimization System (COS) that has been developed using real time Electric Vehicle (EV) and Wind energy data. The paper takes consideration of DB Anonymizer GE software for data anonymization with Smart Grid data use case and without Smart Grid data. In addition, the implementation of EV data anonymization and robustness of its anonymization strategy set is evaluated and described in the paper, along with the lessons learned and the potential for future improvements to the data anonymization strategy determination. The novelty of the mechanism itself stems from the effective evaluation of the GE for Smart Grid environment and hence, enhances the privacy preservation capabilities of the Charging Optimization System.

AB - We present an approach to adopt the DB Anonymizer (Database Anonymizer) GE (Generic Enabler) in the context of a case study relating to a Smart Grid Charging Optimization System (COS) that has been developed using real time Electric Vehicle (EV) and Wind energy data. The paper takes consideration of DB Anonymizer GE software for data anonymization with Smart Grid data use case and without Smart Grid data. In addition, the implementation of EV data anonymization and robustness of its anonymization strategy set is evaluated and described in the paper, along with the lessons learned and the potential for future improvements to the data anonymization strategy determination. The novelty of the mechanism itself stems from the effective evaluation of the GE for Smart Grid environment and hence, enhances the privacy preservation capabilities of the Charging Optimization System.

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DO - 10.1007/978-3-319-12574-9_8

M3 - Article

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SN - 1865-0929

VL - 470

SP - 89

EP - 96

JO - Communications in Computer and Information Science

JF - Communications in Computer and Information Science

ER -

Smart grid data anonymization for smart grid privacy

Abstract

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