Survey on securing data storage in the cloud

Cloud Computing has become a well-known primitive nowadays; many researchers and companies are embracing this fascinating technology with feverish haste. In the meantime, security and privacy challenges are brought forward while the number of cloud storage user increases expeditiously. In this work, we conduct an in-depth survey on recent research activities of cloud storage security in association with cloud computing. After an overview of the cloud storage system and its security problem, we focus on the key security requirement triad, i.e., data integrity, data confidentiality, and availability. For each of the three security objectives, we discuss the new unique challenges faced by the cloud storage services, summarize key issues discussed in the current literature, examine, and compare the existing and emerging approaches proposed to meet those new challenges, and point out possible extensions and futuristic research opportunities. The goal of our paper is to provide a state-of-the-art knowledge to new researchers who would like to join this exciting new field

Secure and Efficient Utilization of Encrypted Cloud Data using Multi-Keyword Ranked Search

Cloud Computing is a technology that provides services to users such as software as a service, platform as a service and storage as a service. These services are provided based on Pay-per-Use basis so these services are cost effective and flexible. Due to this advantage of cloud computing, the individuals as well as the enterprises are getting motivated to shift their local sensitive and huge data management system to cloud storage. But the sensitive data has to be encrypted before outsourcing in order to provide security to the data. After the data has outsourced it has to be utilized efficiently without losing the originality as it was stored. In this paper we provide a mechanism called ”Multi-keyword Ranked Search over Encrypted cloud data” that gives better and efficient searched result over the encrypted data taking multiple keywords as query, which obsoletes the tradition searching scheme based on plain text search. And we use a “Coordinate Matching” technique to find as many matches as possible and use “inner product similarity” to retrieve relevance search results. So if user wants to retrieve the data stored on cloud, he can specify the multiple keywords and rank for relevance retrieval of results. Finally results the user with top ranked files. DOI: 10.17762/ijritcc2321-8169.160415

Secure k-Nearest Neighbor Query over Encrypted Data in Outsourced Environments

For the past decade, query processing on relational data has been studied extensively, and many theoretical and practical solutions to query processing have been proposed under various scenarios. With the recent popularity of cloud computing, users now have the opportunity to outsource their data as well as the data management tasks to the cloud. However, due to the rise of various privacy issues, sensitive data (e.g., medical records) need to be encrypted before outsourcing to the cloud. In addition, query processing tasks should be handled by the cloud; otherwise, there would be no point to outsource the data at the first place. To process queries over encrypted data without the cloud ever decrypting the data is a very challenging task. In this paper, we focus on solving the k-nearest neighbor (kNN) query problem over encrypted database outsourced to a cloud: a user issues an encrypted query record to the cloud, and the cloud returns the k closest records to the user. We first present a basic scheme and demonstrate that such a naive solution is not secure. To provide better security, we propose a secure kNN protocol that protects the confidentiality of the data, user's input query, and data access patterns. Also, we empirically analyze the efficiency of our protocols through various experiments. These results indicate that our secure protocol is very efficient on the user end, and this lightweight scheme allows a user to use any mobile device to perform the kNN query.Comment: 23 pages, 8 figures, and 4 table