Proactive Batch Authentication: Fishing Counterfeit RFID Tags in Muddy Waters

Abstract

Radio frequency identification technology has been recently employed as an effective solution for anti-counterfeiting. By attaching a tag on each product, a reader can provide a fast and automatic authentication. However, most of existing approaches adopt a per-tag approach to validate each product, which will incur a long scanning time and communication traffic when there are many products to be concurrently authenticated. To address these issues, batch authentication, e.g., SEBA, is proposed in recent literature. Batch authentication is an identification-free and probabilistic approach and aims to authenticate the validity of a batch of tags. Unfortunately, the existing batch authentication still suffers a serious scalability problem where the system cost linearly depends on the total number of genuine tags, e.g., ${\mathcal {O}(N)}$ . Its efficiency will significantly decrease and even be worse than the per-tag approach when there is a large number of genuine tags in the system. This limitation hinders the batch authentication to be widely used in practice because there are up to millions of total genuine tags in usual. In this paper, we propose FISH, which is also probabilistic batch authentication-based, to overcome the scalability problem by reducing the dependence to ${\mathcal {O}(\log (N))}$ . Moreover, FISH owns the following two salient advantages: 1) FISH can identify the genuine products when there is no counterfeit product in the batch although it is identification-free in essence and 2) FISH can defend against silence attack by utilizing Pearson Chi-square test. Last, we also conduct the theoretical analysis and extensive simulation with the real logistics trace