On Efficient and Robust Anonymization for Privacy Protection on Massive Streaming Categorical Information

Abstract

Protecting users' privacy when transmitting a large amount of data over the Internet is becoming increasingly important nowadays. In this paper, we focus on the streaming categorical information and propose a novel anonymization technique for providing a strong privacy protection to safeguard against privacy disclosure and information tampering. Our technique utilizes an innovative two-phase anonymization approach which is very easy to implement, highly efficient in terms of speed and communication and is robust against possible tampering from adversaries. Extensive experimental evaluation that is conducted demonstrates that our technique is very efficient and more robust than the existing method.