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

小型企业创新研究(SBIR)第二阶段项目的更广泛影响/商业潜力将是创建一种新工具，该工具可以防止存储在大数据管理系统中的敏感数据因网络攻击而丢失。此外，拟议的网络安全工具可以允许组织审计其大数据使用情况，以防止数据滥用并遵守各种隐私法规。最近的攻击表明，存储数据的泄露/窃取可能会导致巨大的金钱损失和对组织声誉的损害，并增加个人身份被盗的风险。此外，在大数据时代，保护存储数据的安全和隐私对于维护公众信任和从收集的数据中获得充分价值至关重要。该公司提议的工具将通过解决大数据安全和隐私方面的这些重要社会需求，潜在地产生重大影响。根据客户发现结果，该工具还将满足许多不同行业的重要客户需求，并随着越来越多的公司采用大数据技术而具有重大商业影响的潜力。这一小型企业创新研究第二阶段项目将把一种新型的大数据隐私、安全和治理管理工具商业化，该工具提供高效的数据清理、基于属性的访问控制、问责和治理政策执行功能，以保护存储在大数据管理系统中的敏感数据。此外，拟议的产品将提供新颖的数据敏感度入侵检测功能。第二阶段的研究目标是：1)开发有效的基于属性的访问控制框架，以防止对敏感数据的未经授权访问；2)开发数据清理能力，以遵守各种法规；3)开发可扩展的审计日志捕获、存储和查询框架，以加强对大数据使用的问责；以及4)开发数据敏感度感知入侵检测框架，以快速检测针对敏感数据的潜在攻击。这些目标在不影响公司现有工作流程的情况下扩展到大数据方面构成了重大的研究挑战。该公司建议通过使用新颖的代码注入技术结合风险感知审计日志生成和基于数据敏感性的机器学习入侵检测技术来应对这些挑战。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.