In this project, we propose similarity search techniques for sensitive metric data, e.g., bioinformatics data that enable outsourcing of such search. Here, we consider a cloud computing process, in which similarity querying of metric data is outsourced to a service provider. The data is to be accepted only to trusted users, not to the service provider or anyone else. Users query the server for the most similar data objects to a query example. Here, we use Metric Preservation technique. The proposed Metric Preserving Transformation stores relative distance information at the server with respect to a private set of anchor objects. In query processing phase, the data owner specifies a key value in order to define the instance to be used. In a preprocessing phase, the data owner computes id for each object p and uploads it to the server (i.e., service provider). At query time, the query user specifies his query object q and then submits the transformed query object q0 to the server for similarity search. This method guarantees correctness of the final search result, but at the cost of two rounds of communication. Our techniques provide interesting trade-offs between query cost and accuracy. The proposed Flexible Distance-based Hashing methods finishes in just a single round of communication. They are then further extended to offer an intuitive privacy guarantee. Our techniques are capable of offering privacy while enabling efficient and accurate processing of similarity queries. Using FNN (Fast Nearest Neighbor) algorithm, based on the distance, the nearest neighbor is calculated. Finally, the results of query processing much more efficient compare to previous algorithms. It offers the data owner very scalability.