Perfect secrecy cryptography via mixing of chaotic waves in irreversible time-varying silicon chips

Falco, Andrea Di; Mazzone, Valerio; Cruz, A; Fratalocchi, Andrea

doi:10.1038/s41467-019-13740-y

Cited by 35 publications

(30 citation statements)

References 55 publications

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“…Nowadays, artificial intelligence is permeating the field of photonics [55,56] and viceversa [57]. Optical platforms are enabling computing functionalities with unique features, ranging from photonic Ising machines for complex optimization problems [58][59][60][61][62][63][64][65] to optical devices for cryptography [66]. We have realized a novel photonic neuromorphic computing device that is able to perform classification and feature extraction only by exploiting optical propagation in free space.…”

Section: Discussionmentioning

confidence: 99%

Photonic extreme learning machine by free-space optical propagation

2021

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Photonic brain-inspired platforms are emerging as novel analog computing devices, enabling fast and energyefficient operations for machine learning. These artificial neural networks generally require tailored optical elements, such as integrated photonic circuits, engineered diffractive layers, nanophotonic materials, or time-delay schemes, which are challenging to train or stabilize. Here, we present a neuromorphic photonic scheme, i.e., the photonic extreme learning machine, which can be implemented simply by using an optical encoder and coherent wave propagation in free space. We realize the concept through spatial light modulation of a laser beam, with the far field acting as a feature mapping space. We experimentally demonstrate learning from data on various classification and regression tasks, achieving accuracies comparable with digital kernel machines and deep photonic networks. Our findings point out an optical machine learning device that is easy to train, energetically efficient, scalable, and fabrication-constraint free. The scheme can be generalized to a plethora of photonic systems, opening the route to real-time neuromorphic processing of optical data.

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Section: Discussionmentioning

confidence: 99%

Photonic extreme learning machine by free-space optical propagation

2021

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“…Manufacturing process variations are among the most technologically relevant forms of such randomness and are the primary means by which PUFs extract their chip-unique signatures [1][2][3][8][9][10]. Recently, classical time-varying PUFs have been applied in secure communications [11] and in schemes obtaining perfect secrecy cryptography [12]. In another highly demanded application, PUFs have emerged as promising security primitives for authentication and identification throughout the untrusted supply chain as illustrated in Figure 1.…”

Section: Main Textmentioning

confidence: 99%

Robust optical physical unclonable function using disordered photonic integrated circuits

et al. 2020

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AbstractPhysical unclonable function (PUF) has emerged as a promising and important security primitive for use in modern systems and devices, due to their increasingly embedded, distributed, unsupervised, and physically exposed nature. However, optical PUFs based on speckle patterns, chaos, or ‘strong’ disorder are so far notoriously sensitive to probing and/or environmental variations. Here we report an optical PUF designed for robustness against fluctuations in optical angular/spatial alignment, polarization, and temperature. This is achieved using an integrated quasicrystal interferometer (QCI) which sensitively probes disorder while: (1) ensuring all modes are engineered to exhibit approximately the same confinement factor in the predominant thermo-optic medium (e. g. silicon), and (2) constraining the transverse spatial-mode and polarization degrees of freedom. This demonstration unveils a new means for amplifying and harnessing the effects of ‘weak’ disorder in photonics and is an important and enabling step toward new generations of optics-enabled hardware and information security devices.

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“…In this field of research, photonics is pioneering technologies for different lines of applications, including authentication [17,18], secure communications [19][20][21], and classical equivalent schemes to quantum key distribution with perfect secrecy [22]. The main advantage of photonics PUFs is strong device unclonability: while cloning electronic PUF implementations has been reported [23], no one was ever able to replicate an optical PUF.…”

Section: Introductionmentioning

confidence: 99%

“…The issue is the local correlations that are present in the PUF response: when transformed into a binary string with conventional techniques, a certain degree of correlation remains in the key and between different keys [24]. To the best of the authors' knowledge, with the exception of the study by Di Falco et al [22], no optical PUFs has been verified against certification standards that guarantee the genuine unpredictability and uncorrelation of the keys, and no technique has been devised to address this problem controllably for optical PUFs.…”

Section: Introductionmentioning

confidence: 99%

NIST-certified secure key generation via deep learning of physical unclonable functions in silica aerogels

Fratalocchi¹,

Fleming²,

Conti³

et al. 2021

Frontiers in Optics and Photonics

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Physical unclonable functions (PUFs) are complex physical objects that aim at overcoming the vulnerabilities of traditional cryptographic keys, promising a robust class of security primitives for different applications. Optical PUFs present advantages over traditional electronic realizations, namely, a stronger unclonability, but suffer from problems of reliability and weak unpredictability of the key. We here develop a two-step PUF generation strategy based on deep learning, which associates reliable keys verified against the National Institute of Standards and Technology (NIST) certification standards of true random generators for cryptography. The idea explored in this work is to decouple the design of the PUFs from the key generation and train a neural architecture to learn the mapping algorithm between the key and the PUF. We report experimental results with all-optical PUFs realized in silica aerogels and analyzed a population of 100 generated keys, each of 10,000 bit length. The key generated passed all tests required by the NIST standard, with proportion outcomes well beyond the NIST's recommended threshold. The two-step key generation strategy studied in this work can be generalized to any PUF based on either optical or electronic implementations. It can help the design of robust PUFs for both secure authentications and encrypted communications.

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Perfect secrecy cryptography via mixing of chaotic waves in irreversible time-varying silicon chips

Cited by 35 publications

References 55 publications

Photonic extreme learning machine by free-space optical propagation

Photonic extreme learning machine by free-space optical propagation

Robust optical physical unclonable function using disordered photonic integrated circuits

NIST-certified secure key generation via deep learning of physical unclonable functions in silica aerogels

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