Smart homes are IoT systems enabling the automation of household operation. The unrestricted collection and processing of data by smart home systems raises legitimate privacy concerns for their users. Over the past decade, there has been significant interest in privacy-enhancing technologies applied at the level of a local smart hub physically located in the home and acting as a gateway between sensors, applications, platform providers, and services in the cloud. The number and variety of projects and research proposals can, however, make their comparison a daunting and unnecessarily complex task. We systematize existing knowledge in this field through the analysis and categorization of 10 industrial and community-contributed systems and 37 research proposals from the literature of the past 11 years. Our results shed light on the diversity of system and trust models considered in the state-of-the-art and on the associated privacy-enhancing technologies. We further identify open research problems and promising approaches that would benefit the smart home hub model and the protection of smart home users’ privacy.

Internet of Things (IoT) device software frequently handles sensitive data. This software has to be resistant to faults to prevent leakage and ensure data privacy and security. Source code hardening is a common way to make software fault-tolerant. However, the effectiveness and performance impact of a chosen hardening technique are not always obvious. Moreover, it becomes increasingly difficult to predict potential attack vectors and implement proper countermeasures. To assist in this task, we developed Chaos Duck, an automatic tool for IoT software fault-tolerance analysis. Chaos Duck emulates various fault types and provides statistics on their impact on software security and stability. We present a case study in which we use Chaos Duck to compare five software hardening techniques applied to the PRESENT block cipher implementation. We show that some simple hardening techniques may improve fault-tolerance, while others can instead reduce overall security and introduce new vulnerabilities. Our contributions are twofold: we offer a software fault-tolerance analysis tool to IoT developers seeking to make their software secure and robust, and we shed light on the efficiency of various hardening techniques.

Smart home technology has gained widespread adoption. However, several instances of massive corporate surveillance and episodes of sensor data breaches have raised many privacy concerns amongst potential consumers. This paper presents PatrIoT, a private-by-design IoT platform for smart home environments. PatrIoT revisits the typical architecture of existing IoT platforms, and provides an alternative design where the home owner retains full ownership and control of smart device generated data. It leverages Intel SGX to prevent unauthorized access to the data by untrusted IoT cloud providers, and offers homeowners an intuitive security abstraction named flowwall which allows them to specify easy-to-use policies for controlling sensitive sensor data flows within their smart homes. We have built and evaluated a PatrIoT prototype. Most of the participants in a field study considered PatrIoT to be easy to use, and the supported policies to be useful in protecting their privacy.

Software security is a fundamental dimension in the development of mobile applications (apps). Since many apps have access to sensitive data (e.g., collected from a smartphone's sensors), the presence of security vulnerabilities may put that data in danger and lead to privacy violations. Unfortunately, existing security solutions for Android are either too cumbersome to use by common app developers, or may require the modification of Android OS. This paper presents Flowverine, a system for building privacy-sensitive mobile apps for unmodified Android platforms. Flowverine exposes an API based on a dataflow programming model which allows for efficient taint tracking of sensitive data flows within each app. By checking such flows against a security policy, Flowverine can then prevent potential privacy violations. We implemented a prototype of our system. Our evaluation shows that Flowverine can be used to implement mobile applications that handle security-sensitive information flows while preserving compatibility with existing Android OS and incurring small performance overheads.