An Additive Order And Privacy Preserving Function Family (AOPPF)

The plentiful benefits of cloud computing, for privacy concerns, individuals and enterprise users are disinclined to outsource their susceptible data, including emails, personal health records and government confidential files, to the cloud. This is as once sensitive data are outsourced to a inaccessible cloud, the analogous data owners lose direct control of these data. We identify a multi-owner model for privacy preserving keyword search over encrypted cloud data. We recommend an capable data user , which not only prevents attackers from eavesdropping secret keys and imaginary to be illegal data users performing searches, but also facilitate data user certification and revocation.

Private search over big data leveraging distributed file system and parallel processing

As the new technologies recently became widespread, enormous amount of data started to be generated in very high speeds and stored in untrusted servers. The big data concept covers not only the exceptional size of the datasets, but also high data generation rate and large variety of data types. Although the Big Data provides very tempting benefits, the security issues are still an open problem. In this thesis, we identify security and privacy problems associated with a certain big data application, namely secure keyword-based search over encrypted cloud data and emphasize the actual challenges and technical difficulties in the big data setting. More specifically, we provide definitions from which privacy requirements can be derived. In addition, we adapt an existing work on privacy-preserving keyword-based search method, which is one of the fundamental operations that can be performed over encrypted data, to the big data setting, in which, not only data is huge but also changing and accumulating very fast. Therefore, in the big data setting, a secure index that allows search over encrypted data should be constructed and updated very fast in addition to an efficient and effective keyword-based search operation method. Our proposal is scalable in the sense that it can leverage distributed file systems and parallel programming techniques such as the Hadoop Distributed File System (HDFS) and the MapReduce programming model to work with very large datasets. We also propose a lazy idf-updating method that can efficiently handle the relevancy scores of the documents in dynamically changing and large datasets. We empirically show the efficiency and accuracy of the method through extensive set of experiments on real dat

Implementation of searchable symmetric encryption for privacy-preserving keyword search on cloud storage

Ensuring the cloud data security is a major concern for corporate cloud subscribers and in some cases for the private cloud users. Confidentiality of the stored data can be managed by encrypting the data at the client side before outsourcing it to the remote cloud storage server. However, once the data is encrypted, it will limit server’s capability for keyword search since the data is encrypted and server simply cannot make a plaintext keyword search on encrypted data. But again we need the keyword search functionality for efficient retrieval of data. To maintain user’s data confidentiality, the keyword search functionality should be able to perform over encrypted cloud data and additionally it should not leak any information about the searched keyword or the retrieved document. This is known as privacy preserving keyword search. This paper aims to study privacy preserving keyword search over encrypted cloud data. Also, we present our implementation of a privacy preserving data storage and retrieval system in cloud computing. For our implementation, we have chosen one of the symmetric key primitives due to its efficiency in mobile environments. The implemented scheme enables a user to store data securely in the cloud by encrypting it before outsourcing and also provides user capability to search over the encrypted data without revealing any information about the data or the query