In image retrieval search engine, the images can be retrieved based on the text or content. The existing image retrieval which classifies based on the click through logs may plays a vital role for effective search results. The user satisfaction can be obtained through the user click sequence. From the click sequence, the feedback sessions are calculated and produce effective search results. Thus the results show only the related results, but the users need the related and relevant data results. For this, the novel algorithm may propose to obtain the effective relevant and related search results based on both the click through logs and personalized web search. The personal profile added to the system to get the effective search results that are based on both these two techniques. After that the privacy protection has been implemented to avoid the data leakage when the system use protected details for searching. Our runtime generalization aims at striking a balance between two predictive metrics that evaluate the utility of personalization and the privacy risk of exposing the generalized profile. The new system presents two greedy algorithms, namely GreedyDP and GreedyIL, for runtime generalization. The system proposes a Cluster-Based SVM (CB-SVM) method to overcome the problems obtained with the SVM Classifier and also it can be tested with the Big Data Applications. We also provide an online prediction mechanism for deciding whether personalizing a query is beneficial. The experimental results show the effectiveness of the novel algorithm.
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Image Retrieval, click through logs, Cluster Based SVM Classifier, data leakage.