Paper 2017/1212

A New Crypto-Classifier Service for Energy Efficiency in Smart Cities

Oana Stan, Mohamed-Haykel Zayani, Renaud Sirdey, Amira Ben Hamida, Alessandro Ferreira Leite, and Mallek Mziou-Sallami

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 efficiency. 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 labeling 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-classification 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.