A secure arbiter physical unclonable functions (PUFs) for device authentication and identification

Recent fourth industrial revolution, industry4.0 results in lot of automation of industrial processes and brings intelligence in many home appliances in the form of IoT, enhances M2M / D2D communication where electronic devices play a prominent role. It is very much necessary to ensure security of those devices. To provide reliable authentication and identification of each device and to abort the counterfeiting from the unauthorized foundries Physical Unclonable Functions (PUFs) emerged as a one of the promising cryptographic hardware security solution. PUF is function, mathematically modeled by using uncontrollable/ unavoidable random variances of the fabrication process of the ICs. These variances can generate unpredictable, random responses can be used to overcome the difficulties such as storing the keys in non-volatile memories (NVMs) in the classical cryptography. A wide variety of PUF architectures such as Arbiter PUFs, Ring oscillator PUFs, SRAM PUFs proposed by authors. But due to its design complexity and low cost, Delay based Arbiter PUFs (D-PUFs) are considering to be a one of the security primitives in authentication applications such as low-cost IoT devices for secure key generation. This paper presents a review on the different types of Delay based PUF architectures proposed by the various authors, sources to exhibit the physical disorders in ICs, methods to estimate the Performance metrics and applications of PUF in different domains

Physical Unclonable Function Reliability on Reconfigurable Hardware and Reliability Degradation with Temperature and Supply Voltage Variations

A hardware security solution using a Physical Unclonable Function (PUF) is a promising approach to ensure security for physical systems. PUF utilizes the inherent instance-specific parameters of physical objects and it is evaluated based on the performance parameters such as uniqueness, reliability, randomness, and tamper evidence of the Challenge and Response Pairs (CRPs). These performance parameters are affected by operating conditions such as temperature and supply voltage variations. In addition, PUF implementation on Field Programmable Gate Array (FPGA) platform is proven to be more complicated than PUF implementation on Application-Specific Integrated Circuit (ASIC) technologies. The automatic placement and routing of logic cells in FPGA can affect the performance of PUFs due to path delay imbalance. In this work, the impact of power supply and temperature variations, on the reliability of an arbiter PUF is studied. Simulation results are conducted to determine the effects of these varying conditions on the CRPs. Simulation results show that ± 10% of power supply variation can affect the reliability of an arbiter PUF by about 51%, similarly temperature fluctuation between -40 0C and +60 0C reduces the PUF reliability by 58%. In addition, a new methodology to implement a reliable arbiter PUF on an FPGA platform is presented. Instead of using an extra delay measurement module, the Chip Planner tool for FPGA is used for manually placement to minimize the path delay misalignment to less than 8 ps

Extended Abstract: Analysis of 1000 Arbiter PUF based RFID Tags

In this extended abstract a large-scale analysis of 4- way Arbiter PUFs is performed with measurement results from 1000 RFID tags. Arbiter PUFs are one of the most important building blocks in PUF-based protocols and have been the subject of many papers. However, in the past often only software simulations or a limited number of test chips were available for analysis. Therefore, the goal of this work is to verify earlier findings in regard to the uniqueness and reliability of Arbiter PUFs by using a much larger measurement set. Furthermore, we used machine learning algorithms to approximate and compare the internal delay differences of the employed PUF. One of the main research questions in this paper is to examine if any “outliers” occurred, i.e., if some tags performed considerably different. This might for example happen due to some unusual manufacturing variations or faults. However, our findings are that for all of the analyzed tags the parameters fell within the range of a Gaussian distribution without significant outliers. Hence, our results are indeed in line with the results of prior work