Probabilistic yoking proofs for large scale IoT systems

Yoking (or grouping) proofs were introduced in 2004 as a security construction for RFID applications in which it is needed to build an evidence that several objects have been scanned simultaneously or, at least, within a short time. Such protocols were designed for scenarios where only a few tags (typically just two) are involved, so issues such as preventing an object from abandoning the proof right after being interrogated simply do not make sense. The idea, however, is very interesting for many Internet of Things (IoT) applications where a potentially large population of objects must be grouped together. In this paper we address this issue by presenting the notion of Probabilistic Yoking Proofs (PYP) and introducing three main criteria to assess their performance: cost, security, and fairness. Our proposal combines the message structure found in classical grouping proof constructions with an iterative Poisson sampling process where the probability of each object being sampled varies over time. We introduce a number of mechanisms to apply fluctuations to each object’s sampling probability and present different sampling strategies. Our experimental results confirm that most strategies achieve good security and fairness levels while keeping the overall protocol cost down.