Abstract
In recent years, physically unclonable functions (PUFs) have gained significant attraction in IoT security applications, such as cryptographic key generation and entity authentication. PUFs extract the uncontrollable production characteristics of different devices to generate unique fingerprints for security applications. When generating PUF-based secret keys, the reliability and entropy of the keys are vital factors. This study proposes a novel method for generating PUF-based keys from a set of measurements. Firstly, it formulates the group-based key generation problem as an optimization problem and solves it using integer linear programming (ILP), which guarantees finding the optimum solution. Then, a novel scheme for the extraction of keys from groups is proposed, which we call positioning syndrome coding (PSC). The use of ILP as well as the introduction of PSC facilitates the generation of high-entropy keys with low error correction costs. These new methods have been tested by applying them on the output of a capacitor network PUF. The results confirm the application of ILP and PSC in generating high-quality keys.
Highlights
The internet of things (IoT) has already changed our way of living and the way we interact with a range of devices, such as cars, home appliances, and wearables [1]
One important security aspect of IoT devices is the security of data, such as microprocessor program data, sensor data, or communication data [2]
Authors in [17] proposed the longest increasing sequence-based algorithm (LISA) to enhance secret key generation by grouping physically unclonable functions (PUFs) measurement results. They have introduced compact syndrome coding (CSC) to generate keys from the groups that resulted from the LISA
Summary
The internet of things (IoT) has already changed our way of living and the way we interact with a range of devices, such as cars, home appliances, and wearables [1]. Unclonable functions (PUF) have proven to be a potentially robust solution for such IoT security needs [3]. They leverage the inherent features of hardware to generate unique fingerprints for security applications. We propose an optimum key generation method for the cases where the raw output of the PUF is a set of analog measurements. The so-called grouping algorithm has been proposed in the literature as a means of deriving longer keys from a set of PUF measurements. No evidence is provided to show that the grouping algorithm finds the optimum solution to maximize the length of the generated key.
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