Abstract

Radio-Frequency IDentification (RFID) is an emerging technology that employs radio waves to identify, locate, and track objects. RFID technology has wide applications in many areas including manufacturing, healthcare, and transportation. However, the manipulation of uniquely identifiable objects gives rise to privacy concerns for the individuals carrying these objects. Most previous works on privacy-preserving RFID technology, such as EPC re-encryption and killing tags, have focused on the threats caused by the physical RFID tags in the data collection phase, but these techniques cannot address privacy threats in the data publishing phase, when a large volume of RFID data is released to a third party. We explore the privacy threats in RFID data publishing. We illustrate that even though explicit identifying information, such as phone numbers and SSNs, is removed from the published RFID data, an attacker may still be able to perform privacy attacks by utilizing background knowledge about a target victim's visited locations and timestamps. Privacy attacks include identifying a target victim's record and/or inferring their sensitive information. High-dimensionality is an inherent characteristic in RFID data; therefore, applying traditional anonymity models, such as K -anonymity, to RFID data would significantly reduce data utility. We propose a new privacy model, devise an anonymization algorithm to address the special challenges of RFID data, and experimentally evaluate the performance of our method. Experiments suggest that applying our model significantly improves the data utility when compared to applying the traditional K -anonymity model.