SBIR Phase II: A Security, Privacy and Governance Policy Enforcement Framework for Big Data

Data Security Technologies Llc, Blacksburg VA

Investigators

Abstract

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project will be the creation of a new tool that could prevent the loss of sensitive data stored in big data management systems due to cyber-attacks. Furthermore, the proposed cybersecurity tool can allow organizations to audit their big data usage to prevent data misuse and comply with various privacy regulations. Recent attacks have shown that the leakage/stealing of stored data may result in enormous monetary loss and damage to organizational reputation, and increased identity theft risks for individuals. Furthermore, in the age of big data, protecting the security and privacy of stored data is paramount for maintaining public trust, and getting the full value from the collected data. The company's proposed tool will potentially have significant impact by addressing these important societal needs with respect to big data security and privacy. Based on customer discovery findings, this tool will also address an important customer need found in many different industries and has the potential to have significant commercial impact as more and more companies are adopting big data technologies. This Small Business Innovation Research Phase II project will commercialize a novel big data privacy, security and governance management tool that provides efficient data sanitization, attribute-based access control, accountability and governance policy enforcement capabilities for protecting sensitive data stored in big data management systems. In addition, the proposed product will provide novel data sensitivity aware intrusion detection capabilities. The Phase II research objectives are: 1) to develop an efficient attribute-based access control framework to prevent unauthorized access to sensitive data; 2) to develop data sanitization capabilities for complying with various regulations; 3) to develop a scalable audit log capture, storage and querying framework for increasing accountability for big data usage; and 4) to develop a data sensitivity aware intrusion detection framework to quickly detect potential attacks against sensitive data. These objectives pose significant research challenges with respect to scaling to big data without impacting the existing workflow of the companies. The company proposes to address these challenges by using novel code injection techniques combined with risk aware audit log generation and data sensitivity aware machine learning based intrusion detection techniques. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.