Biblio

Privacy preservation is a challenging task with the huge amount of data that are available in social media. The data those are stored in the distributed environment or in cloud environment need to ensure confidentiality to data. In addition, representing the voluminous data is graph will be convenient to perform keyword search. The proposed work initially reads the data corresponding to social media and converts that into a graph. In order to prevent the data from the active attacks Advanced Encryption Standard algorithm is used to perform graph encryption. Later, search operation is done using two algorithms: kNK keyword search algorithm and top k nearest keyword search algorithm. The first scheme is used to fetch all the data corresponding to the keyword. The second scheme is used to fetch the nearest neighbor. This scheme increases the efficiency of the search process. Here shortest path algorithm is used to find the minimum distance. Now, based on the minimum value the results are produced. The proposed algorithm shows high performance for graph generation and searching and moderate performance for graph encryption.

Searchable Encryption (SE) schemes provide security and privacy to the cloud data. The existing SE approaches enable multiple users to perform search operation by using various schemes like Broadcast Encryption (BE), Attribute-Based Encryption (ABE), etc. However, these schemes do not allow multiple users to perform the search operation over the encrypted data of multiple owners. Some SE schemes involve a Proxy Server (PS) that allow multiple users to perform the search operation. However, these approaches incur huge computational burden on PS due to the repeated encryption of the user queries for transformation purpose so as to ensure that users' query is searchable over the encrypted data of multiple owners. Hence, to eliminate this computational burden on PS, this paper proposes a secure proxy server approach that performs the search operation without transforming the user queries. This approach also returns the top-k relevant documents to the user queries by using Euclidean distance similarity approach. Based on the experimental study, this approach is efficient with respect to search time and accuracy.