Enhancing Personalized Web Search Protection Using Cryptography Algorithm

Over the last twenty years, there has been a extensive growth in the amount of private data collected about individuals. This data comes from a number of sources including medical, financial, library, telephone, and shopping records. Such data can be integrated and analyzed digitally as it's possible due to the rapid growth in database, networking, and computing technologies. On the one hand, this has led to the development of data mining tools that aim to infer useful trends from this data. But, on the other hand, easy access to personal data poses a threat to individual privacy. On the other hand privacy regulations and other privacy concerns may prevent data owners from sharing information for data analysis. In order to share data while preserving privacy data owner must come up with a solution which achieves the dual goal of privacy preservation as well as accurate clustering result. Some experimental results are presented which tries to finds the optimum value of segment size and quantization parameter which gives optimum in the tradeoff between clustering utility and data privacy in the input dataset. This research work protects the information about PWS applications that model user preferences as hierarchical user profiles. In this paper, proposes a PWS framework called UPS that can daptively generalize profiles by queries while respecting user specified privacy requirements. It aims at providing protection against a typical model of privacy attack using the cryptography algorithm.