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The hallmark of the information age is the ease with which information is stored, accessed, and shared throughout the globe. This is enabled, in large part, by the simplicity of duplicating digital information without error. Unfortunately, an ever-growing consequence is the global threat to security and privacy enabled by our digital reliance. Specifically, modern secure communications and authentication suffer from formidable threats arising from the potential for copying of secret keys stored in digital media. With relatively little transfer of information, an attacker can impersonate a legitimate user, publish malicious software that is automatically accepted as safe by millions of computers, or eavesdrop on countless digital exchanges. To address this vulnerability, a new class of cryptographic devices known as physical unclonable functions (PUFs) are being developed. PUFs are modern realizations of an ancient concept, the physical key, and offer an attractive alternative for digital key storage. A user derives a digital key from the PUF’s physical behavior, which is sensitive to physical idiosyncrasies that are beyond fabrication tolerances. Thus, unlike conventional physical keys, a PUF cannot be duplicated and only the holder can extract the digital key. However, emerging machine learning (ML) methods are remarkably adept at learning behavior via training, and if such algorithms can learn to emulate a PUF, then the security is compromised. Unfortunately, such attacks are highly successful against conventional electronic PUFs. Here, we investigate ML attacks against a nonlinear silicon photonic PUF, a novel design that leverages nonlinear optical interactions in chaotic silicon microcavities. First, we investigate these devices’ resistance to cloning during fabrication and demonstrate their use as a source of large volumes of cryptographic key material. Next, we demonstrate that silicon photonic PUFs exhibit resistance to state-of-the-art ML attacks due to their nonlinearity and finally validate this resistance in an encryption scenario.
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Reconfigurable Multilevel Optical PUF by Spatiotemporally Programmed Crystallization of Supersaturated Solution
Abstract Physical unclonable functions (PUFs) are emerging as an alternative to information security by providing an advanced level of cryptographic keys with non‐replicable characteristics, yet the cryptographic keys of conventional PUFs are not reconfigurable from the ones assigned at the manufacturing stage and the overall authentication process slows down as the number of entities in the dataset or the length of cryptographic key increases. Herein, a supersaturated solution‐based PUF (S‐PUF) is presented that utilizes stochastic crystallization of a supersaturated sodium acetate solution to allow a time‐efficient, hierarchical authentication process together with on‐demand rewritability of cryptographic keys. By controlling the orientation and the average grain size of the sodium acetate crystals via a spatiotemporally programmed temperature profile, the S‐PUF now includes two global parameters, that is, angle of rotation and divergence of the diffracted beam, in addition to the speckle pattern to produce multilevel cryptographic keys, and these parameters function as prefixes for the classification of each entity for a fast authentication process. At the same time, the reversible phase change of sodium acetate enables repeated reconfiguration of the cryptographic key, which is expected to offer new possibilities for a next‐generation, recyclable anti‐counterfeiting platform.
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- PAR ID:
- 10419222
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Advanced Materials
- Volume:
- 35
- Issue:
- 22
- ISSN:
- 0935-9648
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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