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Perturbation-Based Fuzzified K-Mode Clustering Method for Privacy Preserving Recommender System

Perturbation-Based Fuzzified K-Mode Clustering Method for Privacy Preserving Recommender System

Abhaya Kumar Sahoo (Kalinga Institute of Industrial Technology (Deemed), India), Srishti Raj (Kalinga Institute of Industrial Technology (Deemed), India), Chittaranjan Pradhan (Kalinga Institute of Industrial Technology (Deemed), India), Bhabani Shankar Prasad Mishra (Kalinga Institute of Industrial Technology (Deemed), India), Rabindra Kumar Barik (Kalinga Institute of Industrial Technology (Deemed), India), and Ankit Vidyarthi (Jaypee Institute of Information Technology, India)
Copyright: © 2022 |Volume: 16 |Issue: 1 |Pages: 20
ISSN: 1930-1650|EISSN: 1930-1669|EISBN13: 9781683180203|DOI: 10.4018/IJISP.2022010115
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MLA

Sahoo, Abhaya Kumar, et al. "Perturbation-Based Fuzzified K-Mode Clustering Method for Privacy Preserving Recommender System." IJISP vol.16, no.1 2022: pp.1-20. https://doi.org/10.4018/IJISP.2022010115

APA

Sahoo, A. K., Raj, S., Pradhan, C., Mishra, B. S., Barik, R. K., & Vidyarthi, A. (2022). Perturbation-Based Fuzzified K-Mode Clustering Method for Privacy Preserving Recommender System. International Journal of Information Security and Privacy (IJISP), 16(1), 1-20. https://doi.org/10.4018/IJISP.2022010115

Chicago

Sahoo, Abhaya Kumar, et al. "Perturbation-Based Fuzzified K-Mode Clustering Method for Privacy Preserving Recommender System," International Journal of Information Security and Privacy (IJISP) 16, no.1: 1-20. https://doi.org/10.4018/IJISP.2022010115

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Abstract

Recommender systems are extensively used today to ease out the problem of information overload and facilitate the product selection by users in e-commerce market. Both privacy and security are two major concerns of the user in these systems. For the protection of the user’s rating, there are several existing works on the basis of encryption or randomization methodologies. This paper proposes a methodology that not only protects the privacy of ratings but also provides better accuracy. After applying fuzzification on the user ratings, random rotation and perturbation methods are used before being fed to the collaborative filtering system. In this process, similar users are grouped into clusters by which recommendation is made. By considering different cluster size on four different datasets, the proposed fuzzified k-Mode clustering method provides less MAE and RMSE value as compared to other k-Means and k-Mode clustering approach and also achieves the better privacy than randomized perturbation method by obtaining IVDM value i.e. 0.67, 0.61, 0.55 and 0.7.

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