SECURED SEARCHING OF VALUABLE DATA IN A METRIC SPACE BASED ON SIMILARITY MEASURE

The aim of the project is collecting the similarity queries from various users and stored in the database. In this paper, we are mainly concentrating on privacy only. Here data owner, service provider, trusted clients are used. Here it is able to maintain data confidentiality with respect to untrusted parties including the service provider. Data owner and service provider and trusted client are used. Data owner is one who stores the data in the database. Here service provider is the third party who maintains the data in the database. Trusted client is one who needs the data from the database. In this paper the data owner provide the privacy to the sensitive information .Here I took medical related information so I collected the medical related data and stored in my database such as fever, headache and diabetes disease related information. Here all the data’s are stored in the hierarchical order in a subject wise or age wise or disease wise. The cloud computing setting in which similarity querying of metric data is outsourced to a service provider. Only authorized users are allowed to access the data. Nobody else including the service provider should be able to view the data. So that data will be kept private. Based on the queries it will be revealed to the trusted users alone. This transformation technique offers perfect data privacy for the data owner but it gives the final result at multiple rounds of communication. This technique also provides an interesting trade-off between query cost and accuracy. Existing solutions either offer query efficiency at no privacy or they offer complete data privacy while sacrificing query efficiency. But the proposed methods are very secure and efficient.