Web search engines are widely used to find huge amount of data on the web in a minimum amount of time. But sometimes it becomes difficult to the users to get exact result for given query. Personalized Web Search (PWS) provides the better search results for individual user needs and improves the quality of the search result based on the user profile. However, user's unwillingness to disclose their private information during search has become major issue in wide increase in PWS. This paper presents a scalable way for users to build rich hierarchical user profiles automatically and provide privacy for the user profiles. Extended User customizable Privacy-preserving Search (E-UPS) framework generalize the user profiles for each query according to user-specified privacy requirements. It will improve the search quality and hide the privacy contents existence in the user profile and gives protection against a typical model of privacy attack.

In this paper, the Accident Reduction Model (ARM) technique has been used to analyze different critical criteria in various industries. This ARM technique is used to determine the conclusions of the decision-making process. Valid data is obtained in the structure of the IoT with proper and consistent and useful information. The network address utility allows efficient sensor data. The necessary configuration procedure effectively monitors relevant sensor boundary values. Finally, we have ensured that the system will be able to provide dynamic performance in an IoT-based use of low-cost estimates and lower execution time.

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