Information-theoretic security and computational security are fundamental paradigms of security in the theory of cryptography. The two paradigms interact with each other but have shown different progress, which motivates us to explore the intersection between them. In this paper, we focus on Multi-Party Computation (MPC) because the security of MPC is formulated by simulation-based security, which originates from computational security, even if it requires information-theoretic security. We provide several equivalent formalizations of the security of MPC under a semi-honest model from the viewpoints of information theory and statistics. The interpretations of these variants are so natural that they support the other aspects of simulation-based security. Specifically, the variants based on conditional mutual information and sufficient statistics are interesting because security proofs for those variants can be given by information measures and factorization theorem, respectively. To exemplify this, we show several security proofs of BGW (Ben-Or, Goldwasser, Wigderson) protocols, which are basically proved by constructing a simulator.

Adversarial examples (AEs), which cause misclassification by adding subtle perturbations to input images, have been proposed as an attack method on image-classification systems using deep neural networks (DNNs). Physical AEs created by attaching stickers to traffic signs have been reported, which are a threat to traffic-sign-recognition DNNs used in advanced driver assistance systems. We previously proposed an attack method for generating a noise area on images by superimposing an electrical signal on the mobile industry processor interface and showed that it can generate a single adversarial mark that triggers a backdoor attack on the input image. Therefore, we propose a misclassification attack method n DNNs by creating AEs that include small perturbations to multiple places on the image by the fault injection. The perturbation position for AEs is pre-calculated in advance against the target traffic-sign image, which will be captured on future driving. With 5.2% to 5.5% of a specific image on the simulation, the perturbation that induces misclassification to the target label was calculated. As the experimental results, we confirmed that the traffic-sign-recognition DNN on a Raspberry Pi was successfully misclassified when the target traffic sign was captured with. In addition, we created robust AEs that cause misclassification of images with varying positions and size by adding a common perturbation. We propose a method to reduce the amount of robust AEs perturbation. Our results demonstrated successful misclassification of the captured image with a high attack success rate even if the position and size of the captured image are slightly changed.

In the field of machine learning security, as one of the attack surfaces especially for edge devices, the application of side-channel analysis such as correlation power/electromagnetic analysis (CPA/CEMA) is expanding. Aiming to evaluate the leakage resistance of neural network (NN) model parameters, i.e. weights and biases, we conducted a feasibility study of CPA/CEMA on floating-point (FP) operations, which are the basic operations of NNs. This paper proposes approaches to recover weights and biases using CPA/CEMA on multiplication and addition operations, respectively. It is essential to take into account the characteristics of the IEEE 754 representation in order to realize the recovery with high precision and efficiency. We show that CPA/CEMA on FP operations requires different approaches than traditional CPA/CEMA on cryptographic implementations such as the AES.

Blockchain-based Internet of Things (IoT) applications require flexible, fast, and low-power hashing hardware to ensure IoT data integrity and maintain blockchain network confidentiality. However, existing hashing hardware poses challenges in achieving high performance and low power and limits flexibility to compute multiple hash functions with different message lengths. This paper introduces the flexible and energy-efficient crypto-processor (FECP) to achieve high flexibility, high speed, and low power with high hardware efficiency for blockchain-based IoT applications. To achieve these goals, three new techniques are proposed, namely the crypto arithmetic logic unit (Crypto-ALU), dual buffering extension (DBE), and local data memory (LDM) scheduler. The experiments on ASIC show that the FECP can perform various hash functions with a power consumption of 0.239-0.676W, a throughput of 10.2-3.35Gbps, energy efficiency of 4.44-14.01Gbps/W, and support up to 8916-bit message input. Compared to state-of-art works, the proposed FECP is 1.65-4.49 times, 1.73-21.19 times, and 1.48-17.58 times better in throughput, energy efficiency, and energy-delay product (EDP), respectively.

The war between cyber attackers and security analysts is gradually intensifying. Owing to the ease of obtaining and creating support tools, recent malware continues to diversify into variants and new species. This increases the burden on security analysts and hinders quick analysis. Identifying malware families is crucial for efficiently analyzing diversified malware; thus, numerous low-cost, general-purpose, deep-learning-based classification techniques have been proposed in recent years. Among these methods, malware images that represent binary features as images are often used. However, no models or architectures specific to malware classification have been proposed in previous studies. Herein, we conduct a detailed analysis of the behavior and structure of malware and focus on PE sections that capture the unique characteristics of malware. First, we validate the features of each PE section that can distinguish malware families. Then, we identify PE sections that contain adequate features to classify families. Further, we propose an ensemble learning-based classification method that combines features of highly discriminative PE sections to improve classification accuracy. The validation of two datasets confirms that the proposed method improves accuracy over the baseline, thereby emphasizing its importance.

In recent years, cyber attacks against infrastructure have become more serious. Unfortunately, infrastructures with vulnerable remote management devices, which allow attackers to control the infrastructure, have been reported. Targeted attacks against infrastructure are conducted manually by human attackers rather than automated scripts. Here, open questions are how often the attacks against such infrastructure happen and what attackers do after intrusions. In this empirical study, we observe the accesses, including attacks and security investigation activities, using the customized infrastructure honeypot. The proposed honeypot comprises (1) a platform that easily deploys real devices as honeypots, (2) a mechanism to increase the number of fictional facilities by changing the displayed facility names on the WebUI for each honeypot instance, (3) an interaction mechanism with visitors to infer their purpose, and (4) tracking mechanisms to identify visitors for long-term activities. We implemented and deployed the honeypot for 31 months. Our honeypot observed critical operations, such as changing configurations of a remote management device. We also observed long-term access to WebUI and Telnet service of the honeypot.

This paper presents an FPGA implementation of real-time high dynamic range (HDR) synthesis, which expresses a wide dynamic range by combining multiple images with different exposures using image pyramids. We have implemented a pipeline that performs streaming processing on images without using external memory. However, implementation for high-resolution images has been difficult due to large memory usage for line buffers. Therefore, we propose an image compression algorithm based on adaptive differential pulse code modulation (ADPCM). Compression modules based on the algorithm can be easily integrated into the pipeline. When the image resolution is 4K and the pyramid depth is 7, memory usage can be halved from 168.48% to 84.32% by introducing the compression modules, resulting in better quality.

Multivariate public key cryptosystems (MPKC) are constructed based on the problem of solving multivariate quadratic equations (MQ problem). Among various multivariate schemes, UOV is an important signature scheme since it is underlying some signature schemes such as MAYO, QR-UOV, and Rainbow which was a finalist of NIST PQC standardization project. To analyze the security of a multivariate scheme, it is necessary to analyze the first fall degree or solving degree for the system of polynomial equations used in specific attacks. It is known that the first fall degree or solving degree often relates to the Hilbert series of the ideal generated by the system. In this paper, we study the Hilbert series of the UOV scheme, and more specifically, we study the Hilbert series of ideals generated by quadratic polynomials used in the central map of UOV. In particular, we derive a prediction formula of the Hilbert series by using some experimental results. Moreover, we apply it to the analysis of the reconciliation attack for MAYO.

Public key authenticated encryption with keyword search (PAEKS) has been proposed, where a sender's secret key is required for encryption, and a trapdoor is associated with not only a keyword but also the sender. This setting allows us to prevent information leakage of keyword from trapdoors. Liu et al. (ASIACCS 2022) proposed a generic construction of PAEKS based on word-independent smooth projective hash functions (SPHFs) and PEKS. In this paper, we propose a new generic construction of PAEKS, which is more efficient than Liu et al.'s in the sense that we only use one SPHF, but Liu et al. used two SPHFs. In addition, for consistency we considered a security model that is stronger than Liu et al.'s. Briefly, Liu et al. considered only keywords even though a trapdoor is associated with not only a keyword but also a sender. Thus, a trapdoor associated with a sender should not work against ciphertexts generated by the secret key of another sender, even if the same keyword is associated. That is, in the previous definitions, there is room for a ciphertext to be searchable even though the sender was not specified when the trapdoor is generated, that violates the authenticity of PAKES. Our consistency definition considers a multi-sender setting and captures this case. In addition, for indistinguishability against chosen keyword attack (IND-CKA) and indistinguishability against inside keyword guessing attack (IND-IKGA), we use a stronger security model defined by Qin et al. (ProvSec 2021), where an adversary is allowed to query challenge keywords to the encryption and trapdoor oracles. We also highlight several issues associated with the Liu et al. construction in terms of hash functions, e.g., their construction does not satisfy the consistency that they claimed to hold.

Identity-based encryption with equality test (IBEET) is a variant of identity-based encryption (IBE), in which any user with trapdoors can check whether two ciphertexts are encryption of the same plaintext. Although several lattice-based IBEET schemes have been proposed, they have drawbacks in either security or efficiency. Specifically, most IBEET schemes only satisfy selective security, while public keys of adaptively secure schemes in the standard model consist of matrices whose numbers are linear in the security parameter. In other words, known lattice-based IBEET schemes perform poorly compared to the state-of-the-art lattice-based IBE schemes (without equality test). In this paper, we propose a semi-generic construction of CCA-secure lattice-based IBEET from a certain class of lattice-based IBE schemes. As a result, we obtain the first lattice-based IBEET schemes with adaptive security and CCA security in the standard model without sacrificing efficiency. This is because, our semi-generic construction can use several state-of-the-art lattice-based IBE schemes as underlying schemes, e.g. Yamada's IBE scheme (CRYPTO'17).

We propose how to homomorphically evaluate arbitrary univariate and bivariate integer functions such as division. A prior work proposed by Okada et al. (WISTP'18) uses polynomial evaluations such that the scheme is still compatible with the SIMD operations in BFV and BGV schemes, and is implemented with the input domain ℤ257. However, the scheme of Okada et al. requires the quadratic numbers of plaintext-ciphertext multiplications and ciphertext-ciphertext additions in the input domain size, and although these operations are more lightweight than the ciphertext-ciphertext multiplication, the quadratic complexity makes handling larger inputs quite inefficient. In this work, first we improve the prior work and also propose a new approach that exploits the packing method to handle the larger input domain size instead of enabling the SIMD operation, thus making it possible to work with the larger input domain size, e.g., ℤ215 in a reasonably efficient way. In addition, we show how to slightly extend the input domain size to ℤ216 with a relatively moderate overhead. Further we show another approach to handling the larger input domain size by using two ciphertexts to encrypt one integer plaintext and applying our techniques for uni/bivariate function evaluation. We implement the prior work of Okada et al., our improved version of Okada et al., and our new scheme in PALISADE with the input domain ℤ215, and confirm that the estimated run-times of the prior work and our improved version of the prior work are still about 117 days and 59 days respectively while our new scheme can be computed in 307 seconds.

Secure two-party computation is a cryptographic tool that enables two parties to compute a function jointly without revealing their inputs. It is known that any function can be realized in the correlated randomness (CR) model, where a trusted dealer distributes input-independent CR to the parties beforehand. Sometimes we can construct more efficient secure two-party protocol for a function g than that for a function f, where g is a restriction of f. However, it is not known in which case we can construct more efficient protocol for domain-restricted function. In this paper, we focus on the size of CR. We prove that we can construct more efficient protocol for a domain-restricted function when there is a “good” structure in CR space of a protocol for the original function, and show a unified way to construct a more efficient protocol in such case. In addition, we show two applications of the above result: The first application shows that some known techniques of reducing CR size for domain-restricted function can be derived in a unified way, and the second application shows that we can construct more efficient protocol than an existing one using our result.

Homomorphic encryption (HE) is public key encryption that enables computation over ciphertexts without decrypting them. To overcome an issue that HE cannot achieve IND-CCA2 security, the notion of keyed-homomorphic encryption (KH-PKE) was introduced (Emura et al., PKC 2013), which has a separate homomorphic evaluation key and can achieve stronger security named KH-CCA security. The contributions of this paper are twofold. First, recall that the syntax of KH-PKE assumes that homomorphic evaluation is performed for single operations, and KH-CCA security was formulated based on this syntax. Consequently, if the homomorphic evaluation algorithm is enhanced in a way of gathering up sequential operations as a single evaluation, then it is not obvious whether or not KH-CCA security is preserved. In this paper, we show that KH-CCA security is in general not preserved under such modification, while KH-CCA security is preserved when the original scheme additionally satisfies circuit privacy. Secondly, Catalano and Fiore (ACM CCS 2015) proposed a conversion method from linearly HE schemes into two-level HE schemes, the latter admitting addition and a single multiplication for ciphertexts. In this paper, we extend the conversion to the case of linearly KH-PKE schemes to obtain two-level KH-PKE schemes. Moreover, based on the generalized version of Catalano-Fiore conversion, we also construct a similar conversion from d-level KH-PKE schemes into 2d-level KH-PKE schemes.

This paper considers the problem of balancing traceability and anonymity in designated verifier signatures (DVS), which are a kind of group-oriented signatures. That is, we propose claimable designated verifier signatures (CDVS), where a signer is able to claim that he/she indeed created a signature later. Ordinal DVS does not provide any traceability, which could indicate too strong anonymity. Thus, adding claimability, which can be seen as a sort of traceability, moderates anonymity. We demonstrate two generic constructions of CDVS from (i) ring signatures, (non-ring) signatures, pseudorandom function, and commitment scheme, and (ii) claimable ring signatures (by Park and Sealfon, CRYPTO'19).

Multi-signatures have seen renewed interest due to their application to blockchains, e.g., BIP 340 (one of the Bitcoin improvement proposals), which has triggered the proposals of several new schemes with improved efficiency. However, many previous works have a “loose” security reduction (a large gap between the difficulty of the security assumption and breaking the scheme) or depend on strong idealized assumptions such as the algebraic group model (AGM). This makes the achieved level of security uncertain when instantiated in groups typically used in practice, and it becomes unclear for developers how secure a given scheme is for a given choice of security parameters. Thus, this leads to the question “what kind of schemes can we construct that achieves tight security based on standard assumptions?”. In this paper, we show a simple two-round tightly-secure pairing-based multi-signature scheme based on the computation Diffie-Hellman problem in the random oracle model. This proposal is the first two-round multi-signature scheme that achieves tight security based on a computational assumption and supports key aggregation. Furthermore, our scheme reduce the signature bit size by 19% compared with the shortest existing tightly-secure DDH-based multi-signature scheme. Moreover, we implemented our scheme in C++ and confirmed that it is efficient in practice; to complete the verification takes less than 1[ms] with a total (computational) signing time of 13[ms] for under 100 signers. The source code of the implementation is published as OSS.

Recently, multivariate time-series data has been generated in various environments, such as sensor networks and IoT, making anomaly detection in time-series data an essential research topic. Unsupervised learning anomaly detectors identify anomalies by training a model on normal data and producing high residuals for abnormal observations. However, a fundamental issue arises as anomalies do not consistently result in high residuals, necessitating a focus on the time-series patterns of residuals rather than individual residual sizes. In this paper, we present a novel framework comprising two serialized anomaly detectors: the first model calculates residuals as usual, while the second one evaluates the time-series pattern of the computed residuals to determine whether they are normal or abnormal. Experiments conducted on real-world time-series data demonstrate the effectiveness of our proposed framework.

Non-autoregressive generation has attracted more and more attention due to its fast decoding speed. Latent alignment objectives, such as CTC, are designed to capture the monotonic alignments between the predicted and output tokens, which have been used for machine translation and sentence summarization. However, our preliminary experiments revealed that CTC performs poorly on document abstractive summarization, where a high compression ratio between the input and output is involved. To address this issue, we conduct a theoretical analysis and propose Hierarchical Latent Alignment (HLA). The basic idea is a two-step alignment process: we first align the sentences in the input and output, and subsequently derive token-level alignment using CTC based on aligned sentences. We evaluate the effectiveness of our proposed approach on two widely used datasets XSUM and CNNDM. The results indicate that our proposed method exhibits remarkable scalability even when dealing with high compression ratios.

Pedestrian attribute recognition (PAR) aims to recognize a series of a person's semantic attributes, e.g., age, gender, which plays an important role in video surveillance. This paper proposes a multi-correlation graph convolutional network named MCGCN for PAR, which includes a semantic graph, visual graph, and synthesis graph. We construct a semantic graph by using attribute features with semantic constraints. A graph convolution is employed, based on prior knowledge of the dataset, to learn the semantic correlation. 2D features are projected onto visual graph nodes and each node corresponds to the feature region of each attribute group. Graph convolution is then utilized to learn regional correlation. The visual graph nodes are connected to the semantic graph nodes to form a synthesis graph. In the synthesis graph, regional and semantic correlation are embedded into each other through inter-graph edges, to guide each other's learning and to update the visual and semantic graph, thereby constructing semantic and regional correlation. On this basis, we use a better loss weighting strategy, the suit_polyloss, to address the imbalance of pedestrian attribute datasets. Experiments on three benchmark datasets show that the proposed approach achieves superior recognition performance compared to existing technologies, and achieves state-of-the-art performance.

Composing choreography is challenging because it involves numerous iterative refinements. According to our video analysis and interviews, choreographers typically need to imagine dancers' movements to revise drafts on paper since testing new movements and formations with actual dancers takes time. To address this difficulty, we present an interactive group-dance simulation interface, DanceUnisoner, that assists choreographers in composing a group dance in a simulated environment. With DanceUnisoner, choreographers can arrange excerpts from solo-dance videos of dancers throughout a three-dimensional space. They can adjust various parameters related to the dancers in real time, such as each dancer's position and size and each movement's timing. To evaluate the effectiveness of the system's parametric, visual, and interactive interface, we asked seven choreographers to use it and compose group dances. Our observations, interviews, and quantitative analysis revealed their successful usage in iterative refinements and visual checking of choreography, providing insights to facilitate further computational creativity support for choreographers.