Preserving Privacy and Utility in RFID Data Publishing

Radio Frequency IDentification (RFID) is a technology that helps machines identify objects remotely. The RFID technology has been extensively used in many domains, such as mass transportation and healthcare management systems. The collected RFID data capture the detailed movement information of the tagged objects, offering tremendous opportunities for mining useful knowledge. Yet, publishing the raw RFID data for data mining would reveal the specific locations, time, and some other potentially sensitive information of the tagged objects or individuals. In this paper, we study the privacy threats in RFID data publishing and show that traditional anonymization methods are not applicable for RFID data due to its challenging properties: high-dimensional, sparse, and sequential. Our primary contributions are (1) to adopt a new privacy model called LKC-privacy that overcomes these challenges, and (2) to develop an efficient anonymization algorithm to achieve LKC-privacy while preserving the information utility for data mining.