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Authors: Oana Stan 1 ; Mohamed-Haykel Zayani 2 ; Renaud Sirdey 1 ; Amira Ben Hamida 2 ; Alessandro Ferreira Leite 2 and Mallek Mziou-Sallami 2

Affiliations: 1 CEA, France ; 2 IRT SystemX, France

Keyword(s): Smart City, Secure Classification, Data Privacy, Homomorphic Encryption.

Related Ontology Subjects/Areas/Topics: Case Studies ; Case Studies and Innovative Applications for Smart(Er) Cities ; Computer-Supported Education ; Energy and Economy ; Health Engineering and Technology Applications ; Information Technologies Supporting Learning ; Neural Rehabilitation ; Neurotechnology, Electronics and Informatics ; Security and Privacy ; Service Innovation and Design to Support Smart Cities ; Simulation and Modeling ; Simulation Tools and Platforms ; Smart Cities

Abstract: Smart Cities draw a nice picture of a connected city where useful services and data are ubiquitous, energy is properly used and urban infrastructures are well orchestrated. Fulfilling this vision in our cities implies unveiling citizens data and assets. Thus, security and data privacy appear as crucial issues to consider. In this paper, we study a way of offering a secured energy management service for diagnosis and classification of buildings in a district upon their energy consumption. Our remote service can be beneficial both for local authorities and householders without revealing private data. Our framework is designed such that the private data is permanently encrypted and that the server performing the classification algorithm has no information about the sensitive data and no capability to decrypt it. The underlying cryptographic technology used is homomorphic encryption, allowing to perform calculations directly on encrypted data. We present here the prototype of a crypto-cl assification service for energy consumption profiles involving different actors of a smart city community, as well as the associated performances results. We assess our proposal atop of real data taken from an Irish residential district and we show that our service can achieve acceptable performances in terms of security, execution times and memory requirements. (More)

CC BY-NC-ND 4.0

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Paper citation in several formats:
Stan, O.; Zayani, M.; Sirdey, R.; Ben Hamida, A.; Ferreira Leite, A. and Mziou-Sallami, M. (2018). A New Crypto-classifier Service for Energy Efficiency in Smart Cities. In Proceedings of the 7th International Conference on Smart Cities and Green ICT Systems - SMARTGREENS; ISBN 978-989-758-292-9; ISSN 2184-4968, SciTePress, pages 78-88. DOI: 10.5220/0006697500780088

@conference{smartgreens18,
author={Oana Stan. and Mohamed{-}Haykel Zayani. and Renaud Sirdey. and Amira {Ben Hamida}. and Alessandro {Ferreira Leite}. and Mallek Mziou{-}Sallami.},
title={A New Crypto-classifier Service for Energy Efficiency in Smart Cities},
booktitle={Proceedings of the 7th International Conference on Smart Cities and Green ICT Systems - SMARTGREENS},
year={2018},
pages={78-88},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006697500780088},
isbn={978-989-758-292-9},
issn={2184-4968},
}

TY - CONF

JO - Proceedings of the 7th International Conference on Smart Cities and Green ICT Systems - SMARTGREENS
TI - A New Crypto-classifier Service for Energy Efficiency in Smart Cities
SN - 978-989-758-292-9
IS - 2184-4968
AU - Stan, O.
AU - Zayani, M.
AU - Sirdey, R.
AU - Ben Hamida, A.
AU - Ferreira Leite, A.
AU - Mziou-Sallami, M.
PY - 2018
SP - 78
EP - 88
DO - 10.5220/0006697500780088
PB - SciTePress