ProTECting: An Application of Local Differential Privacy for IoT at the Edge in Smart Home Scenarios

Abstract

With the growth of the Internet of Things (IoT) and Smart Homes, there is an ever-growing amount of data coming from within people's houses. These data are valuable for analysis and to discover patterns in order to improve services and produce resources more efficiently, e.g., using smart meter data to generate energy with less waste. Despite their high value for analysis, these data are intrinsically private and should be treated carefully. IoT data are fundamentally infinite, and this property makes it even more challenging to apply conventional models to achieve privacy. In this work, we propose a differentially private strategy to estimate frequencies of values in the context of Smart Home data, considering the infinite property of the data and focusing on getting better utility than state of the art.