Philip S. Yu, Computer Science Primary Grant Support : NSF IIS-0914934, CNS-1115234
Problem Statement and Motivation • The large amount of data being captured, and digitized has made privacy an important issue
Anonymization
• In many cases, users are not willing to divulge personal information unless privacy is assured • Many industries need to access vast amount of personal data to advance the products or services, e.g. from personalized medicine to product recommendation
k2-degree anonymization (k=2) Technical Approach • To preserve privacy on network/graph data • Not only node attributes, but also connection information need to be anonymized or perturbed
Key Achievements and Future Goals • Identified the friendship attack in a network, where the degrees of two vertices connected by an edge is utilized to reidentify related victims in a published network and devise the k2-degree anonymization technique.
• Identify weakness of current privacy protection methods
• Devise new privacy attack models
• Proposed the concept of structural diversity to protect the anonymity of the network community identities and develop the k-SDA technique
• Develop novel privacy protection methods accordingly • Received EDBT 2014 Test of time award