Privacy Engineering in Smart Home (SH) Systems: A Comprehensive Privacy Threat Analysis and Risk Management Approach

TL;DR

The paper addresses privacy threats in Smart Home (SH) systems, with a special emphasis on device-identity privacy and gaps in existing preservation approaches. It adopts the LINDDUN PRO Privacy Engineering (PE) framework to model SH architecture, perform privacy threat analysis via an interaction-based PTA, and manage risk through Privacy Impact Assessment (PIA) and Privacy-Enhancing Technologies (PETs). It introduces a four-layer SH reference model and a Data Flow Diagram (DFD) to capture data flows, maps threats to DFD interactions, and computes a quantified privacy risk using $PIA = L \times C$ with $L = T_n/T_i$ and $C = I + T_a$. A case study demonstrates PET effectiveness by implementing data masking and end-to-end encryption, resulting in substantial reductions in privacy risk across SH interactions and illustrating practical applicability for vendors, users, researchers, and regulators. The work also discusses trade-offs and the need for lightweight PETs to enable deployment in resource-constrained SH environments and broader IoT contexts.

Abstract

Addressing trust concerns in Smart Home (SH) systems is imperative due to the limited study on preservation approaches that focus on analyzing and evaluating privacy threats for effective risk management. While most research focuses primarily on user privacy, device data privacy, especially identity privacy, is almost neglected, which can significantly impact overall user privacy within the SH system. To this end, our study incorporates privacy engineering (PE) principles in the SH system that consider user and device data privacy. We start with a comprehensive reference model for a typical SH system. Based on the initial stage of LINDDUN PRO for the PE framework, we present a data flow diagram (DFD) based on a typical SH reference model to better understand SH system operations. To identify potential areas of privacy threat and perform a privacy threat analysis (PTA), we employ the LINDDUN PRO threat model. Then, a privacy impact assessment (PIA) was carried out to implement privacy risk management by prioritizing privacy threats based on their likelihood of occurrence and potential consequences. Finally, we suggest possible privacy enhancement techniques (PETs) that can mitigate some of these threats. The study aims to elucidate the main threats to privacy, associated risks, and effective prioritization of privacy control in SH systems. The outcomes of this study are expected to benefit SH stakeholders, including vendors, cloud providers, users, researchers, and regulatory bodies in the SH systems domain.

Figures (8)

• Figure 1: LINDDUN PRO PE Framework LINDDUN
• Figure 2: Layered Architecture of an IoT-Based Smart Home System Illustrating Device, Aggregation, Application, and Event Processing Layers with IoT devices, User, Gateway, and Cloud Interactions represented in each layer
• Figure 3: A reference model of a SH system illustrating the flow of information among users, devices, aggregation, and cloud-based event processing layers. The diagram details processes of interaction, verification, collection, processing, and dissemination for efficient data management.
• Figure 4: Data Flow Diagram (DFD) illustrating the interactions and data exchanges within a SH system. The diagram maps key entities—including user devices, gateways, servers, databases, and third-party services—and details the processes of registration, authentication, commissioning, and data access regulation. The dashed lines represent data and process flows, while the red-dotted boundary line distinguishes device management from user registration processes. The legend clarifies symbols for processes, data stores, external entities, and flow types.
• Figure 5: Sequence diagram illustrating the user access management process in a Smart Home (SH) system before data masking implementation in the user database and end-to-end encryption