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D. Petrellis, A. Leviatan, FRANCESCO IACHELLO

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Tharrmashastha Sapv, Debajyoti Bera, Arpita Maitra +1 more

Michael Partridge, Saloni Jain, Michael Garrett +1 more

The deployment of post-quantum cryptographic algorithms currently under standardization by NIST have the potential to mitigate quantum computer based attacks, which present a whole new range of cybersecurity challenges. We recognize post-quantum cryptography (PQC) key distribution, across autonomous systems operating in contested areas, as a particularly difficult problem to solve. Verifying the origin and integrity of mission instructions is of extreme importance, especially in the context of autonomous vehicles, missiles, and drones; the lack of which can enable opponents to leverage attacks effecting catastrophic damage to life, property, and strategy. This work proposes a protocol for controlled key distribution through the use of PQC algorithms, strengthened by fingerprints of embedded devices. With this framework, we can assure the origin and correctness of digital information sent to autonomous systems, and enforce that only authorized members can send instructions to these devices. This protocol replaces the deterministic, pseudo-random seed in these PQC protocols with a fingerprint derived from the physical disorder of the embedded device to enhance security and maintain the integrity of the system. We demonstrate how this system can be used to send instructions, verify the origin of the instructions upon receipt, and certify the integrity of the instruction packet received. We also discuss situations in which an attacker attempts to falsify information and how to detect that malicious action. We measure the quality and performance of this prototype system by measuring the latency and bit error rates. We demonstrate this prototype system using the CRYSTALS-DILITHIUM digital signature algorithm (DSA).

Kishor Bharti, Tobias Haug, Vlatko Vedral +1 more

The goal of machine learning is to facilitate a computer to execute a specific task without explicit instruction by an external party. Quantum foundations seek to explain the conceptual and mathematical edifice of quantum theory. Recently, ideas from machine learning have successfully been applied to different problems in quantum foundations. Here, the authors compile the representative works done so far at the interface of machine learning and quantum foundations. The authors conclude the survey with potential future directions.