Abstract: Web services provide new types of services leading to an increasing number of services emerging on internet in service and cloud computing. As a result, service-relevant data has become too huge to effectively process and users are finding difficulty to utilize services that match their mash ups. To overcome this challenge technique used is to filter and recognize the similar services under clusters and recommend by calculating their similarities, which is referred from Clustering Based Collaborative Filtering Approach for Recommendation System (ClubRS) technique. User’s data are always privacy sensitive and can be misused by the service provider while generating recommendations. In this paper we aim to protect the private data from the service provider while preserving the functionality of the system. We propose a cryptographic solution for preserving privacy of customer’s data in recommender system. In short, private information of customer is protected and service provider generates recommendation by processing encrypted data. The proposed method is based on homomorphic encryption schemes. Later in the first stage, the encrypted services are divided into small clusters of similar services using agglomerative clustering algorithm (AHC). In the second stage, collaborative filtering algorithm is applied on one of the clusters and recommendations are made using decryption algorithm. Since the number of services in a cluster is much less than the total number of the services provided, it is expected to also enhance the execution time of collaborative filtering.
Keywords: big data application, cluster, collaborative filtering, mashup, homomorphic encryption, privacy.
Title: Privacy Enhanced Clustering Based Collaborative Filtering of Services for Big Data Application
Author: Dr. Ramakrishna V Moni, Akshay M Nandagaon
International Journal of Computer Science and Information Technology Research
ISSN 2348-1196 (print), ISSN 2348-120X (online)
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