Enhanced Privacy Protection in Personalized Web Search for Sequential Background

Personalized Web Search has established to improve the quality of various search services on the Internet. Due to the tremendous data opportunities in the internet the privacy protection is very important to preserve user search behaviours and their profiles. In the existing system two generalized algorithms named as GreedyDP and GreedyIL were applied to protect private data's in Personalized Search Engine. The existing systems failed to resist sequential and background knowledge adversaries who has the broader background knowledge such as richer relationship among topics. The proposed introduces vector quantization approach piecewise on the datasets which segmentize each row of datasets and quantization approach is performed on each segment, using the proposed approach which later are again united to form a transformed data set. The proposed work is implemented using MATLAB and is analyzed using certain parameters such as Precision, Recall, Frequency Measure, Distortion and Computational Delay.