Assigning a misusability weight to a given dataset is strongly related to the way the data is presented (e.g., tabular data, structured or free text) and is domain specific. Therefore, one measure of misusability weight cannot fit all types of data in every domain but it gives a fair idea on how to proceed to handle sensitive data. Previous approaches such as M-score models that consider number of entities, anonymity levels, number of properties and values of properties to estimate misusability value for a data record has better efficiency in deducting record sensitivities. Quality of data, Quantity data, and the distinguishing attributes are vital factors that can influence Mscore. Combined with record ranking and knowledge models prior Approaches used one domain expert for deducting sensitive information. But for better performance and accuracy we propose to use the effect of combining knowledge from several experts (e.g., ensemble of knowledge models). Also we plan to extend the computations of sensitivity level of sensitive attributes to be objectively obtained by using machine learning techniques such as SVM classifier along with expert scoring models. This approach particularly fits the sensitive parameter values to the customer value based on customer activity which is far more efficient compared to face value specification with human involvement. A practical implementation of the proposed system validates our claim. INTRODUCTION:

ABSTRACT:Most users want their search engine to incorporate three key features in query results. Relevant results (results they are actually interested in), Uncluttered (easy to read interface), Helpful options to broaden or tighten a search for accuracy. This paper addresses the third aspect with new improvement measures for an enhanced experience to the end user. A trivial query like purchasing a laptop has to be broken down into a number of co-dependent steps over a period of time based on prior search patterns of the same user .For instance, a user may first search for company and later the features and price. After deciding the company and price the user may then search for the accessories like mouse and modem etc. Each step requires more queries and each query completes with more clicks. Current search engines cannot support this kind of hierarchical queries. We propose to implement Random walk propagation methods that can construct user profiles based on the credentials obtained from their prior search history repositories. Combined with click points driven click graphs of user search behavior the IR system can support complex queries for future requests at reduced navigations. Random walk propagation over the query fusion graph methods support complex search quests in IR systems at reduced times. For developing an interactive IR system we also propose to use these search quests as auto complete features in similar query propagations. Biasing the ranking of search results can also be provided using ranking algorithms (top-k algorithms).Supporting these methods yields dynamic and improved performance in IR systems, by providing enriched user querying experience. A practical implementation of the proposed system validates our claim.