Abstract: Combined physical unclonable function (PUFs); methods, apparatuses, systems, and computer program products for enrolling combined PUFs; and methods, apparatuses, systems, and computer program products for authenticating a device physically associated with a combined PUF are described. In an example embodiment, a combined PUF includes a plurality of PUFs and one or more logic gates. Each PUF includes a plurality of stages and an arbiter configured to generate a single PUF response based on response portions generated by the plurality of stages. The one or more logic gates are configured to combine the single PUF response for each of the plurality of PUFs in accordance with a combination function to provide a combined response.

Abstract: Combined physical unclonable function (PUFs); methods, apparatuses, systems, and computer program products for enrolling combined PUFs; and methods, apparatuses, systems, and computer program products for authenticating a device physically associated with a combined PUF are described. In an example embodiment, a combined PUF includes a plurality of PUFs and one or more logic gates. Each PUF includes a plurality of stages and an arbiter configured to generate a single PUF response based on response portions generated by the plurality of stages. The one or more logic gates are configured to combine the single PUF response for each of the plurality of PUFs in accordance with a combination function to provide a combined response.

Abstract: Embodiments of the present disclosure provide methods, systems, apparatus, and computer program products are for detecting whether a suspect component such as an integrated circuit (IC) or a system-on-chip (SoC) is recycled. Specifically, various embodiments involve processing power supply rejection ratio (PSRR) data obtained from a low drop-out regulator (LDO) used for the suspect component using a recycle detection machine learning model to generate a recycle prediction. In particular embodiments, the recycle detection machine learning model is developed based at least in part on degradation of PSRRs of LDOs. Accordingly, a determination is made as to whether the suspect component is recycled based on the recycle prediction. If so, then an indication that the suspect component is recycled is provided.

Abstract: In general, embodiments of the present disclosure provide methods, apparatus, systems, computer program products, computing devices, computing entities, and/or the like for setting up biometric access for a legitimate user to a design. In accordance with various embodiments, a biometric template is received originating from the user and is inputted to a first secure sketch generator configured to use first transformation parameters comprising a hash function to generate a protected biometric template by hashing the biometric template. The protected biometric template is inputted to a second secure sketch generator configured to use second transformation parameters comprising a physical unclonable function serving as a fingerprint of the design to generate an original obfuscation key from the protected biometric template.

Abstract: Embodiments of the present disclosure provide methods, systems, apparatus, and computer program products are for detecting whether a suspect component such as an integrated circuit (IC) or a system-on-chip (SoC) is recycled. Specifically, various embodiments involve processing power supply rejection ratio (PSRR) data obtained from a low drop-out regulator (LDO) used for the suspect component using a recycle detection machine learning model to generate a recycle prediction. In particular embodiments, the recycle detection machine learning model is developed based at least in part on degradation of PSRRs of LDOs. Accordingly, a determination is made as to whether the suspect component is recycled based on the recycle prediction. If so, then an indication that the suspect component is recycled is provided.