Abstract

Radio Frequency IDentification (RFID) is a technology of automatic object identification. Retailers and manufacturers have created compelling business cases for deploying RFID in their supply chains. Yet, the uniquely identifiable objects pose a privacy threat to individuals. In this paper, we study the privacy threats caused by publishing RFID data. Even if the explicit identifying information, such as name and social security number, has been removed from the published RFID data, an adversary may identify a target victim's record or infer her sensitive value by matching a priori known visited locations and time. RFID data by its nature is high-dimensional and sparse, so applying traditional k -anonymity to RFID data suffers from the curse of high-dimensionality, and results in poor information usefulness. We define a new privacy model and develop an anonymization algorithm to accommodate special challenges on RFID data. Then, we evaluate its effectiveness on synthetic data sets.