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The phenomenal progress of quantum information theory over the last decade has substantially broadened the potential to simulate the superposition of states for exponential speedup of quantum algorithms over their classical peers. Therefore, the conventional and modern cryptographic standards (encryption and authentication) are susceptible to Shor's and Grover's algorithms on quantum computers. The significant improvement in technology permits consummate levels of data protection by encoding classical data into small quantum states that can only be utilized once by leveraging the capabilities of quantum-assisted classical computations. Considering the frequent data breaches and increasingly stringent privacy legislation, we introduce a hybrid quantum-classical model to transform classical data into unclonable states, and we experimentally demonstrate perfect state transfer to exemplify the classical data. To alleviate implementation complexity, we propose an arbitrary quantum signature scheme that does not require the establishment of entangled states to authenticate users in order to transmit and receive arbitrated states to retrieve classical data. The consequences of the probabilistic model indicate that the quantum-assisted classical framework substantially enhances the performance and security of digital data, and paves the way toward real-world applications. [ABSTRACT FROM AUTHOR]

Cryptosystems are commonly deployed to secure data transmission over an insecure line of communication. To provide confusion in the data over insecure networks, substitution boxes are the solitary components for delivering a nonlinear mapping between inputs and outputs. A confusion component of a block cipher with high nonlinearity and low differential and linear approximation probabilities is considered secure against cryptanalysis. This study aims to design a highly nonlinear substitution-permutation network using the blotch symmetry of quantum spin states on the Galois field GF (28). To observe the efficiency of the proposed methodology, some common and advanced measures were evaluated for performance, randomness, and cryptanalytics. The outcomes of these analyses validate that the generated nonlinear confusion components are effective for block ciphers and attain better cryptographic strength with a high signal-to-noise ratio in comparison to state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

Recently, National Institute of Standards and Technology (NIST) in the U.S. had initiated a global-scale competition to standardize the lightweight authenticated encryption with associated data (AEAD) and hash function. Gimli is one of the Round 2 candidates that is designed to be efficiently implemented across various platforms, including hardware (VLSI and FPGA), microprocessors, and microcontrollers. However, the performance of Gimli in massively parallel architectures like Graphics Processing Units (GPU) is still unknown. A high performance Gimli implementation on GPU can be especially useful to Internet of Things (IoT) applications, wherein the gateway devices and cloud servers need to handle a massive number of communications protected by AEAD. In this paper, we show that with careful optimization, Gimli can be efficiently implemented in desktop and embedded GPU to achieve extremely high throughput. Our experiments show that the proposed Gimli implementation can achieve 661.44 KB/s (encryption), 892.24 KB/s (decryption), and 4344.46 KB/s (hashing) in state-of-the-art GPUs. [ABSTRACT FROM AUTHOR]

Many people have used public keys in various areas based on public key infrastructure (PKI). PKI provides a method to publicize public keys securely. However, existing PKI methods have a problem that they assume trusted third parties. Therefore, the existing PKIs cannot be used when users cannot trust certificate issuers. To solve this problem, we propose a new trust model and describe its implementation based on blockchain. Users can trust the certificate issued by full nodes even if they do not trust the full nodes themselves. We analyze the security of our model and show that its security can be achieved higher than existing models. This new model can be particularly useful in an environment where a third party cannot be easily trusted. [ABSTRACT FROM AUTHOR]

Rain streaks can eclipse some information of an image taken during rainfall which can degrade the performance of a vision system. While existing rain removing methods can recover the semantic structure, they lack natural texture recovery. The aim of this work is to recover the hidden structure and texture under the rain streaks with fine details. We propose a novel generative adversarial network with two discriminators to remove rain called rain removal generative adversarial network, where a combination of reconstruction, feature and adversarial losses is used for low level, structural and natural recovery, respectively. We have found that exploiting low-level l 1 loss with high-level structural similarity loss as a reconstruction loss is quite effective in attaining visually plausible and consistent texture. Qualitative and quantitative evaluations on our synthetically created dataset and a benchmark dataset show substantial performance gain than state-of-the-art rain removing methods. [ABSTRACT FROM AUTHOR]

As many services have changed from offline to online, a lot of personal information including user private data has been collected by and exchanged with various service providers. An issue raised in this process is that personal information can be exploited by multiple unwanted entities without the data owner's knowledge. To solve this problem, functional encryption was proposed. It is suitable for data protection because even if a third-party uses the owner's secret key for a function f, it cannot retrieve the original message x from the ciphertext. This means that information about x cannot be published, but is exposed only as f(x), the result of the function f. However, previous pairing-based public key functional encryption schemes for inner product evaluations (FE-IPE) cannot be practical solutions yet because they require too much computation, communication and storage overheads. In this paper, we propose an efficient pairing-based public key FE-IPE that requires only n (i.e., the dimension of vectors for function and message) exponentiation plus two pairing computations for decryption with smaller sized public parameters, secret keys and ciphertexts. And this scheme supports fully collusion resistance. The proposed scheme is proven selectively secure against chosen-plaintext attacks in the standard model under the external Diffie–Hellman assumption. [ABSTRACT FROM AUTHOR]