An Instrument for Measuring Privacy in IoT Environments

Abstract

In an Internet of Things (IoT) environment some problems related to user privacy may occur because the exchange of information between devices occurs in a non-standard way. Brazilian data protection law – intended to protect personal data - must also be observed in applications implemented in the Internet of Things environment. This paper presents the IoTPC instrument, which is a tool for measuring privacy in IoT environments and is able to reflect users’ concerns with privacy. IoTPC consists of 17 items that understand users’ opinions on how some IoT devices collect, process, and make their personal information available in some specific IoT scenarios. The IoTPC tool was used in an inference model of the privacy negotiation mechanism for IoT systems. This model makes inferences based on IoTPC items and IoT scenarios using machine learning algorithms that have been trained and tested with IoTPC privacy preferences. The validation of the instrument was made by analyzing the result of a sample of 61 participants, considering the three first order dimensions (IoT requests, decision making and caution) through an exploratory factor analysis. The results of the learning process in the inference model had an accuracy of 79.20%, which indicates that IoTPC can be used in any privacy negotiation mechanism.