Abstract
The proliferation of smart devices in recent years has led to novel smart home applications that upgrade traditional home appliances to intelligent units and automatically adapt their services without human assistance. In a smart home system, a central gateway is required to coordinate the functions of various smart home devices and allow bidirectional communications. However, the gateway may cause leakage of sensitive information unless proper privacy protections are applied. In this work, we first introduce a smart home model based on fog computing and secured by differential privacy. Then, we apply a personalized differential privacy scheme to provide privacy protection. Furthermore, we consider a collusion attack and propose our differential privacy model called APDP based on a modified Laplace mechanism and a Markov process to strengthen privacy protection, thus resisting the attack. Lastly, we perform extensive experiments based on the real-world datasets to evaluate the proposed APDP model.
Highlights
With the fast development of computing, communication and data science technologies, advanced service systems, such as the smart home, make our life more efficient and convenient
Smart home model based on fog computing and differential privacy: We propose a privacy-preserving smart home model based on the fog computing paradigm and introduce differential privacy to protect privacy in the model
TRUST DISTANCE-BASED DIFFERENTIAL PRIVACY Based on the proposed smart home structure, we further model it using graph theory
Summary
With the fast development of computing, communication and data science technologies, advanced service systems, such as the smart home, make our life more efficient and convenient. The fog server running on such a device provides distributed computation, communication, control and storage to the smart home along with the cloud-to-things continuum [7]–[9]. Massive volumes of data collected from ubiquitous sensors, wearable devices or smart meters may lead to improper release of sensitive personal and private information. With significantly increased computing resources, a fog computing-based smart home provides enhanced smart home applications and introduces more dimensions of privacy preservation into smart home systems. We first develop a smart home model based on fog computing and protected by differential privacy. Our work is one of the first to introduce both differential privacy and fog computing to a smart home application scenario. We propose our APDP model based on a modified Laplace and Markov process to improve privacy protection and resist the attack.
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