Meerkat - A Dynamic Privacy Framework for Web Services
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
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The widespread use of digital data, storage and sharing for data mining has given data snoopers a big opportunity to collect and match records from multiple sources for identity theft and other privacy-invasion activities. While most healthcare organizations do a good job in protecting their data in their databases, very few organizations take enough precautions to protect data that is shared with third party organizations. This data is vulnerable to data hackers, snoopers and rouge employees that want to take advantage of the situation. Only recently has the regulatory environment (like HIPAA) tightened the laws to enforce data and privacy protection. The goal of this project was to explore use of value added software services to counter this invasion of privacy problem when data is shared with an external organization for data mining, statistical analysis or other purposes. Specifically, the goal of this service is to protect data without removing sensitive/non-sensitive attributes. Sophisticated data masking algorithms are used in these services to intelligently perturb and swap data fields making it extremely difficult for data snoopers to reveal personal identity, even after linking records with other data sources. Our software service provides value added data analysis with the masked dataset. Dataset-level properties and statistics remain approximately the same after data masking; however, individual record-level values are changed or perturbed to confuse the data snoopers.