Data Anonymization for Privacy Preservation in Big Data

Cloud computing provides capable ascendable IT edifice to provision numerous processing of a various big data applications in sectors such as healthcare and business. Mainly electronic health records data sets and in such applications generally contain privacy-sensitive data. The most popular technique for data privacy preservation is anonymizing the data through generalization. Proposal is to examine the issue against proximity privacy breaches for big data anonymization and try to recognize a scalable solution to this issue. Scalable clustering approach with two phase consisting of clustering algorithm and K-Anonymity scheme with Generalisation and suppression is intended to work on this problem. Design of the algorithms is done with MapReduce to increase high scalability by carrying out dataparallel execution in cloud. Wide-ranging researches on actual data sets substantiate that the method deliberately advances the competence of defensive proximity privacy breaks, the scalability and the efficiency of anonymization over existing methods. Anonymizing data sets through generalization to gratify some of the privacy attributes like k- Anonymity is a popularly-used type of privacy preserving methods. Currently, the gauge of data in numerous cloud surges extremely in agreement with the Big Data, making it a dare for frequently used tools to actually get, manage, and process large-scale data for a particular accepted time scale. Hence, it is a trial for prevailing anonymization approaches to attain privacy conservation for big data private information due to scalabilty issues

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

In this work, we identify the 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 to the Big Data setting, in which, not only data is huge but also changing and accumulating very fast. 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 data sets. We also propose a lazy idf-updating method that can efficiently handle the relevancy scores of the documents in a dynamically changing, large data set. We empirically show the efficiency and accuracy of the method through extensive set of experiments on real data

Scather: programming with multi-party computation and MapReduce

We present a prototype of a distributed computational infrastructure, an associated high level programming language, and an underlying formal framework that allow multiple parties to leverage their own cloud-based computational resources (capable of supporting MapReduce [27] operations) in concert with multi-party computation (MPC) to execute statistical analysis algorithms that have privacy-preserving properties. Our architecture allows a data analyst unfamiliar with MPC to: (1) author an analysis algorithm that is agnostic with regard to data privacy policies, (2) to use an automated process to derive algorithm implementation variants that have different privacy and performance properties, and (3) to compile those implementation variants so that they can be deployed on an infrastructures that allows computations to take place locally within each participant’s MapReduce cluster as well as across all the participants’ clusters using an MPC protocol. We describe implementation details of the architecture, discuss and demonstrate how the formal framework enables the exploration of tradeoffs between the efficiency and privacy properties of an analysis algorithm, and present two example applications that illustrate how such an infrastructure can be utilized in practice.This work was supported in part by NSF Grants: #1430145, #1414119, #1347522, and #1012798