Functional encryption is a recent generalization of public-key cryptography which aims at enabling secret-key owners to decrypt only functions of the encrypted data. This model is very promising in terms of applications. Yet, although general constructions of theoretical interests do exist, practical functional encryption is presently limited to the evaluation of low-degree functions of the encrypted inputs. In this paper, we investigate how Inner-Product Functional Encryption (IPFE) may enable the design of tax calculation system with built-in privacy. The paper is also concluded by performances results demonstrating the practicality of the approach on the concrete issue of carbon tax calculations.

This paper presents the application of clustering algorithms to daily energy consumption curves of buildings. Our aim is to identify a reduced set of consumption patterns for a tertiary building during one year. These patterns depend on the temperature throughout the year as well as the type of the day (working day, work-free day and school holidays). Two clustering approaches are used independently, namely the K-means algorithm and the Expectation-Maximization algorithm based on Gaussian Mixture Model (EM-GMM). The clustering results obtained with the two algorithms are analyzed and compared. This study represents the first step towards the development of a prediction model for energy consumption.