Privacy protection for RFID data

Benjamin C M Fung, Ming Cao, Bipin C. Desai, Heng Xu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

27 Scopus citations


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 timestamps. RFID data by default is high-dimensional and sparse, so applying traditional K-anonymity to RFID data suffers from the curse of high dimensionality, and would result in poor data usefulness. We define a new privacy model, develop an anonymization algorithm to accommodate special challenges on RFID data, and evaluate its performance in terms of data quality, efficiency, and scalability. To the best of our knowledge, this is the first work on anonymizing high-dimensional RFID data.

Original languageEnglish (US)
Title of host publication24th Annual ACM Symposium on Applied Computing, SAC 2009
Number of pages8
StatePublished - 2009
Event24th Annual ACM Symposium on Applied Computing, SAC 2009 - Honolulu, HI, United States
Duration: Mar 8 2009Mar 12 2009

Publication series

NameProceedings of the ACM Symposium on Applied Computing


Other24th Annual ACM Symposium on Applied Computing, SAC 2009
Country/TerritoryUnited States
CityHonolulu, HI

All Science Journal Classification (ASJC) codes

  • Software


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