A Blockchain-based Privacy-Preserving Recommendation Mechanism

Lin, Liangjie; Tian, Ye; Liu, Yang

doi:10.1109/csp51677.2021.9357604

Cited by 6 publications

(7 citation statements)

References 6 publications

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“…A privacy‐preserving recommendation mechanism by Blockchain to address problems related to processing and analysing data closely related to users' privacy by Lin et al. [7] is presented. However, a fully distributed model that mitigates the risk of privacy disclosure through centralised data storage has been established to prevent the risk of privacy.…”

Section: Methodsmentioning

confidence: 99%

“…There is always the risk of data leakage and security issues; those mentioned applications covered these problems and proposed state of the art of security/privacy algorithms. According to the review and classification of articles [7,12,13,16,18,19], blockchain technology is used to increase and provide system security and privacy in data transfer. In short, a blockchain can act as a distributed ledger with digitally signed data and is auditable; any changes made therein can be traced back to the original data, ensuring security.…”

Section: A Brief Summary Of the Evaluation Of Solutionsmentioning

confidence: 99%

“…This sub-section presents a short analysis of the security/privacy-based IoT applications shown in refs [3,[6][7][8][9][10][11][12][13][14][15][16][17][18][19]…”

mentioning

confidence: 99%

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Recent trends towards privacy‐preservation in Internet of Things, its challenges and future directions

Yaraziz

Jalili

Gheisari

et al. 2022

IET Circuits, Devices & Syst

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The Internet of Things (IoT) is a self-configuring, intelligent system in which autonomous things connect to the Internet and communicate with each other. As 'things' are autonomous, it may raise privacy concerns. In this study, the authors describe the background of IoT systems and privacy and security measures, including (a) approaches to preserving privacy in IoT-based systems, (b) existing privacy solutions, and (c) recommending privacy models for different layers of IoT applications. Based on the results of our study, it is clear that new methods such as Blockchain, Machine Learning, Data Minimisation, and Data Encryption can greatly impact privacy issues to ensure security and privacy. Moreover, it makes sense that users can protect their personal information easier if there is fewer data to collect, store, and share by smart devices. Thus, this study proposes a machine learning-based data minimisation method that, in these networks, can be very beneficial for privacy-preserving.

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Section: Methodsmentioning

confidence: 99%

Section: A Brief Summary Of the Evaluation Of Solutionsmentioning

confidence: 99%

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Recent trends towards privacy‐preservation in Internet of Things, its challenges and future directions

Yaraziz

Jalili

Gheisari

et al. 2022

IET Circuits, Devices & Syst

Self Cite

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“…This is a generic cross-user federated recommendation framework that protects rating and rating behavior. It is also used for rating prediction, item ranking and sequential recommendation (Lin et al , 2021a, Lin et al , 2021b). Federated learning has been proposed for RSs to ensure privacy preservation.…”

Section: Techniquesmentioning

confidence: 99%

“…Lin et al proposed a blockchain-based privacy-preserving recommendation mechanism. In this method, the interplanetary file system combined with blockchain improves communication efficiency and local sensitive hashing, and local differential privacy reduces the computation overhead and ensures privacy (Lin et al , 2021a, Lin et al , 2021b).…”

Section: Techniquesmentioning

confidence: 99%

Privacy-preserving techniques in recommender systems: state-of-the-art review and future research agenda

Pramod

2022

DTA

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PurposeThis study explores privacy challenges in recommender systems (RSs) and how they have leveraged privacy-preserving technology for risk mitigation. The study also elucidates the extent of adopting privacy-preserving RSs and postulates the future direction of research in RS security.Design/methodology/approachThe study gathered articles from well-known databases such as SCOPUS, Web of Science and Google scholar. A systematic literature review using PRISMA was carried out on the 41 papers that are shortlisted for study. Two research questions were framed to carry out the review.FindingsIt is evident from this study that privacy issues in the RS have been addressed with various techniques. However, many more challenges are expected while leveraging technology advancements for fine-tuning recommenders, and a research agenda has been devised by postulating future directions.Originality/valueThe study unveils a new comprehensive perspective regarding privacy preservation in recommenders. There is no promising study found that gathers techniques used for privacy protection. The study summarizes the research agenda, and it will be a good reference article for those who develop privacy-preserving RSs.

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A survey on blockchain-based Recommender Systems: Integration architecture and taxonomy

Mekouar

Iraqi

Damaj

et al. 2022

Computer Communications

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A Blockchain-based Privacy-Preserving Recommendation Mechanism

Cited by 6 publications

References 6 publications

Recent trends towards privacy‐preservation in Internet of Things, its challenges and future directions

Recent trends towards privacy‐preservation in Internet of Things, its challenges and future directions

Privacy-preserving techniques in recommender systems: state-of-the-art review and future research agenda

A survey on blockchain-based Recommender Systems: Integration architecture and taxonomy

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