Security and Privacy Preserving Schemes in Smart Homes using Blockchain

Qashlan, Amjad Mohammed

Security and Privacy Preserving Schemes in Smart Homes using Blockchain

Qashlan, Amjad Mohammed

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Publication Type:: Thesis
Issue Date:: 2021

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Field	Value	Language
dc.contributor.author	Qashlan, Amjad Mohammed
dc.date.accessioned	2022-12-14T00:17:20Z
dc.date.available	2022-12-14T00:17:20Z
dc.date.issued	2021
dc.identifier.uri	http://hdl.handle.net/10453/164376
dc.description	University of Technology Sydney. Faculty of Engineering and Information Technology.	en_US.UTF-8
dc.description.abstract	Emerging technologies such as the Internet of Things, sensors, and communication networks have been integrated into traditional homes to provide a wide range of smart home services to simplify and improve people’s lifestyles. However, as the Internet of Things has grown in popularity, so have the concerns it poses. As a result, concerns like data privacy, security, and decentralisation of IoT systems present substantial threats to the future of smart home IoTs. This thesis presents efforts towards a blockchain-based smart home framework which supports data confidentiality, differential privacy, and robustness. The thesis achieves three novel contributions. We first deploy a private blockchain using Ethereum smart contracts for a smart home to ensure only the homeowner can access and monitor home appliances. The smart contracts are designed to allow devices to communicate without the need for a trusted third party. Our prototype demonstrates three key elements of blockchain-based smart security solutions for smart home applications: smart contracts, blockchain-based access control, and the performance evaluation of the proposed scheme. Next, we propose an authentication scheme that integrates attribute-based access control using smart contracts with an ERC-20 Token (Ethereum Request For Comments) and edge computing to construct a secure framework for IoT devices in a smart home system. The edge server provides scalability to the system by offloading heavier computation tasks to edge servers. We present the system architecture and design and discuss various aspects of testing and implementing smart contracts. Finally, we conduct a performance evaluation to demonstrate the feasibility and efficiency of the proposed scheme. The core features that blockchain technology is leveraged upon are a trust-less environment, immutability and transparency, which come at the cost of a lack of data privacy. Therefore, we propose a privacy-preserving architecture for smart home-based blockchain. The architecture utilises differential privacy machine learning algorithm to send private IoT smart home data to the cloud and achieve data privacy. The main objective of the model is to protect privacy with high accuracy when aggregating the data from traffic analysis, linking and mining attacks by adding Gaussian noise. The implementation of our model ensures better accuracy and improved model utility. The goal of the privacy protection scheme used in our architecture is to enable smart home data to be used without disclosing privacy and provide published data to different service providers with lower information loss and higher data utility.	en_US.UTF-8
dc.format	Thesis (PhD)
dc.language.iso	en_US	en_US.UTF-8
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/164376/2/02whole.pdf
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.rights	au.edu.uts.lib/ppc
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Security and Privacy Preserving Schemes in Smart Homes using Blockchain	en_US.UTF-8
dc.type	Thesis
utslib.copyright.status	open_access	*

Abstract:

Emerging technologies such as the Internet of Things, sensors, and communication networks have been integrated into traditional homes to provide a wide range of smart home services to simplify and improve people’s lifestyles. However, as the Internet of Things has grown in popularity, so have the concerns it poses. As a result, concerns like data privacy, security, and decentralisation of IoT systems present substantial threats to the future of smart home IoTs. This thesis presents efforts towards a blockchain-based smart home framework which supports data confidentiality, differential privacy, and robustness. The thesis achieves three novel contributions. We first deploy a private blockchain using Ethereum smart contracts for a smart home to ensure only the homeowner can access and monitor home appliances. The smart contracts are designed to allow devices to communicate without the need for a trusted third party. Our prototype demonstrates three key elements of blockchain-based smart security solutions for smart home applications: smart contracts, blockchain-based access control, and the performance evaluation of the proposed scheme. Next, we propose an authentication scheme that integrates attribute-based access control using smart contracts with an ERC-20 Token (Ethereum Request For Comments) and edge computing to construct a secure framework for IoT devices in a smart home system. The edge server provides scalability to the system by offloading heavier computation tasks to edge servers. We present the system architecture and design and discuss various aspects of testing and implementing smart contracts. Finally, we conduct a performance evaluation to demonstrate the feasibility and efficiency of the proposed scheme. The core features that blockchain technology is leveraged upon are a trust-less environment, immutability and transparency, which come at the cost of a lack of data privacy. Therefore, we propose a privacy-preserving architecture for smart home-based blockchain. The architecture utilises differential privacy machine learning algorithm to send private IoT smart home data to the cloud and achieve data privacy. The main objective of the model is to protect privacy with high accuracy when aggregating the data from traffic analysis, linking and mining attacks by adding Gaussian noise. The implementation of our model ensures better accuracy and improved model utility. The goal of the privacy protection scheme used in our architecture is to enable smart home data to be used without disclosing privacy and provide published data to different service providers with lower information loss and higher data utility.

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http://hdl.handle.net/10453/164376