A Survey Paper on Secure Privacy Preserving Structure for Content Based Information Retrieval on Large Scale

It is very essential to protect personal confidential data that we share or search through web. Previously there are number of privacy preserving mechanism has been developed. Here we develop a new privacy protection framework for huge- content-based information retrieval. We are offering protection in two layers. Initially, robust hash values are taken as queries to avoid revealing of unique features or content. Then, the client has to select to skip some of the bits in a hash value for increasing the confusion for the server. Since we are reducing information it is not so easy for servers to know about interest of the client. The server needs to give back the hash values of all promising candidates to the client. The client will find the best match by searching in the candidate list. Because we are only sharing hash values between server and client the privacy of client and server will be protected. We begin the idea of tunable privacy, where we can adjust level of privacy protection according to the policy. We can realized it by hash based. It can be realized through piecewise inverted indexing based on hash. We have to divide extracted feature vector into pieces and index each and every piece with a value. Every value is linked with an inverted index list. The framework has been comprehensively tested with very huge image database. We have estimated both privacy-preserving performance and retrieval performance for those content recognition application. Couple of robust hash algorithm is being used. One is based on discrete wavelet transform; the other is based on the random projections. Both of these algorithms demonstrate acceptable recital in association with state-of-the-art retrieval schemes. We believe the bulk voting attack for guesstimate the query recognition and sort. Experiment results confirm that this attack is a peril when there are near-duplicates, but the success rate is depends upon the number of distinct item and omitted bits, success rate decrees when omitted bits are increased

A Novel Approach for Preserving Privacy of Content Based Information Reterival System

Content based information retrieval system (CBIR) are advanced version of retrieval systems where search is based upon specific criteria in order to get relevant items. In networking environment, as search is based on content it is easy for server to know client’s interest, where client has to trust server to get relevant items. Sometimes query contains sensitive information that client does not want to reveal it, but still search should be performed. This is achieved by our proposed structure, where mainly it will deal with multimedia items such as image or audio files. In order to preserve privacy , client selects multimedia file of which hash value is generated, this value is fired towards cloud server. Cloud server contains database of stored hash values of multimedia items and based upon hamming distance and similarity search, encrypted candidate list is prepared and send it to client. Client finds best item by carrying decryption