MapReduce Based Framework for Searchable Encryption
Vijayaraghavan Varadharajan, Sivakumar Kuppusamy, Krishnan Krishnamoorthy · 2016
BigData denotes voluminous amounts of semi structured and / or unstructured dataset that grow enormously and is difficult to handle because of the complexities associated with it. BigData is too large to process using traditional database systems. MapReduce is a framework used for processing BigData in parallel on large number of commodity computers. This model has been an inspiration for the genesis of many parallel computing frameworks like Hadoop. Data outsourcing is a frugal option for organizations which consider building and maintaining their own data management systems. However, precautions have to be taken to ensure that their data is not compromised. It is a challenge to protect the data and safeguard privacy of the users, especially in distributed environments. The focus of this paper is to reduce the overhead of Searchable encryption on Hadoop by minimizing the time taken for encryption of huge volumes of data by processing them in parallel. The encryption and search operations are multi-user supported and the framework is designed to handle addition and revocation of user access privileges with ease of use. The increase in size owing to encryption is offset by enabling compression.