Dynamic Parameters-Based Reversible Data Transform (RDT) Algorithm in Recommendation System

Beg, Saira; Anjum, Adeel; Ahmed, Mansoor; Malik, Saif Ur Rehman; Malik, Hassan; Sharma, Navuday; Waqar, Omer

doi:10.1109/access.2021.3101150

Cited by 8 publications

(8 citation statements)

References 39 publications

(86 reference statements)

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“…The proposed DRWMR system is tested on the movie Lens 100 K dataset, and metrics such as recall, precision, RMSE, MAE, and F1-score are compared to existing approaches such as CTR [ 17 ], DDCF [ 18 ], CRDTM [ 19 ], and HMRNN [ 22 ].…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…'is section contains the experimental setup and a description of the datasets for comparative analysis. (19) where F r denotes the recommendation related to the new user and T r signifies the real amount of related recommendations. (ii) Precision:…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…Beg et al [ 19 ] introduced a chaotic based reversible data transform method (CRDTM) for preserving privacy in data mining for the recommendation system. This method will dynamically produce the values of RDT parameters during processing time, but it does not need the parameters during recovery.…”

Section: Literature Reviewmentioning

confidence: 99%

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A Deep Ranking Weighted Multihashing Recommender System for Item Recommendation

Kumar

Singh

Jain

et al. 2022

Computational Intelligence and Neuroscience

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Collaborative filtering (CF) techniques are used in recommender systems to provide users with specialised recommendations on social websites and in e-commerce. But they suffer from sparsity and cold start problems (CSP) and fail to interpret why they recommend a new item. A novel deep ranking weighted multihash recommender (DRWMR) system is designed to suppress sparsity and CSP. The proposed DRWMR system contains two stages: the neighbours’ formation and recommendation phases. Initially, the data is fed to the deep convolutional neural network (CNN). The significant features are extracted from CNN. The CNN contains an additional layer; the hash code is generated by minimising pairwise ranking loss and classification loss. Therefore, a weight is assigned to different hash tables and hash bits for a recommendation. Then, the similarity between users is obtained based on the weighted hammering distance; the similarity between users helps to form the neighbourhood for the active user. Finally, the rating for unknown items can be obtained by taking the weighted average rating of the neighbourhood, and a list of the top n items can be produced. The effectiveness and accuracy of the proposed DRWMR system are tested on the MovieLens 100 K dataset and compared with the existing methods. Based on the evaluation results, the proposed DRWMR system gives precision (0.16), the root mean squared error (RMSE) of 0.73 and the recall (0.08), the mean absolute error (MAE) of 0.57, and the F − 1 measure (0.101).

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Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Experimental Results and Analysismentioning

confidence: 99%

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A Deep Ranking Weighted Multihashing Recommender System for Item Recommendation

Kumar

Singh

Jain

et al. 2022

Computational Intelligence and Neuroscience

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“…However, the leader election process is straightforward, and leader collision is possible that can breach privacy. However, the same authors in (47) addressed the RDT prior data sharing and its parameter protection challenges by proposing a chaotic RDT for PPDP MRS. The authors also claim that the proposed approach can replace homomorphic encryption techniques and preserve the privacy of the MRS.…”

Section: Literature Surveymentioning

confidence: 99%

An anonymization-based privacy-preserving data collection protocol for digital health data

Andrew

Eunice

Karthikeyan

2023

Front. Public Health

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Digital health data collection is vital for healthcare and medical research. But it contains sensitive information about patients, which makes it challenging. To collect health data without privacy breaches, it must be secured between the data owner and the collector. Existing data collection research studies have too stringent assumptions such as using a third-party anonymizer or a private channel amid the data owner and the collector. These studies are more susceptible to privacy attacks due to third-party involvement, which makes them less applicable for privacy-preserving healthcare data collection. This article proposes a novel privacy-preserving data collection protocol that anonymizes healthcare data without using a third-party anonymizer or a private channel for data transmission. A clustering-based k-anonymity model was adopted to efficiently prevent identity disclosure attacks, and the communication between the data owner and the collector is restricted to some elected representatives of each equivalent group of data owners. We also identified a privacy attack, known as “leader collusion”, in which the elected representatives may collaborate to violate an individual's privacy. We propose solutions for such collisions and sensitive attribute protection. A greedy heuristic method is devised to efficiently handle the data owners who join or depart the anonymization process dynamically. Furthermore, we present the potential privacy attacks on the proposed protocol and theoretical analysis. Extensive experiments are conducted in real-world datasets, and the results suggest that our solution outperforms the state-of-the-art techniques in terms of privacy protection and computational complexity.

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“…A privacy-protected RS (PRIPRO) was proposed for context-aware physical activity recommendation (Sengan et al, 2021). A chaotic Reversible Data Transform (RDT)-based algorithm for privacy-preserving data mining was proposed for PP in RS (Beg et al, 2021). Tabassum and Ahmad (2021) proposed an iterative framework, with privacy preservation in the reciprocal recommendation.…”

Section: Related Workmentioning

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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Dynamic Parameters-Based Reversible Data Transform (RDT) Algorithm in Recommendation System

Cited by 8 publications

References 39 publications

A Deep Ranking Weighted Multihashing Recommender System for Item Recommendation

A Deep Ranking Weighted Multihashing Recommender System for Item Recommendation

An anonymization-based privacy-preserving data collection protocol for digital health data

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

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