Distributed and Scalable Privacy-Preserving Data Mining Techniques for Big Data

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It is impossible to ignore the importance of preserving privacy especially in the era of Big Data in various fields, such as health, business, government and social networks. With the immense growth in the ability to store data, the increased computing power, advances in data analytics, and very large increases in the number of devices and sensors connected to the internet and dedicated networks, there has been an increase in privacy and security risks. Typical privacy-preserving techniques used with small data sets, such as de-identification, access control, secure computation and data encryption cannot be simply used with Big Data. Therefore, it is crucial to create a balance between beneficial uses of Big Data and individual privacy. Dealing with privacy issues in the research area of Big Data, like other aspects of Big Data such as data collection, storage, analysis, and result dissemination, has become a challenge.This research program plans to propose and develop new privacy-preserving techniques and extend the existing ones in data mining and statistical analysis methods that are scalable and incremental, such that they can be practically applied on Big Data, while minimizing the negative effects of applying these techniques on the overall performance, the accuracy and utility of the extracted knowledge. The findings and outputs of this research program will be applied on genome-environment-associations in type 2 diabetes to uncover gene-environment interactions associated with this highly common disease as proof of concept and test using real data.The long-term objective of this research is to develop scalable privacy-preserving methods and protocols for data mining algorithms on Big Data. The research will focus on practical methods and techniques for privacy-preserving protocols on both simulated and real data (type 2 diabetes). The findings will be useful for comparison in similarly complex applications in health, business and government. In order to utilize the type 2 diabetes dataset it will be necessary to preserve the individual’s privacy while allowing meaningful data mining and computational operations.The results will extend the set of secure protocols to cover statistical analysis and data mining methods, and the proposed techniques will be applicable in other areas of health, business, and government where Big Data are used. Therefore, the focus of the short-term objectives will be to propose, design and implement efficient privacy-preserving tools, using new and existing privacy-preserving techniques applied to simulated and type 2 diabetes data. The specific short-term objectives of this research are to (1) indicate which steps (from data gathering to dissemination of results) of Big Data require privacy protection and where currently available privacy-preserving techniques are used; (2) identify the statistical and data-mining techniques that are currently used on genetic data; (3) develop privacy-protected data-mining and computational procedures and algorithms for these applications and (4) test these privacy-protected algorithms on simulated Big Data and type 2 diabetes datasets.