Privacy Presering To Identify De-Duplicate Data With Multi-Pupils Management In Cloud Stroage

With the rapidly increasing amounts of data produced worldwide, networked and multi-user storage systems are becoming very popular. However, concerns over data security still prevent many users from migrating data to remote storage. The conventional solution is to encrypt the data before it leaves the owner’s premises. While sound from a security perspective, this approach prevents the storage provider from effectively applying storage efficiency functions, such as compression and deduplication, which would allow optimal usage of the resources and consequently lower service cost. Client-side data deduplication in particular ensures that multiple uploads of the same content only consume network bandwidth and storage space of a single upload. Deduplication is actively used by a number of cloud backup providers as well as various cloud services. Unfortunately, encrypted data is pseudorandom and thus cannot be deduplicated: as a consequence, current schemes have to entirely sacrifice either security or storage efficiency. we present a scheme that permits a more finegrained trade-off. The intuition is that outsourced data may require different levels of protection, depending on how popular it is: content shared by many users. We present a novel idea that differentiates data according to their popularity. Based on this idea, we design an encryption scheme that guarantees semantic security for unpopular dataand provides weaker security and better storage and bandwidth benefits for popular data. This way, data de-duplication can be effective for popular data, whilst semantically secure encryption protects unpopular content. We can use the backup recover system at the time of blocking and also analyze frequent login access system