Kazuhide Fukushima

Hoang-Quoc Nguyen-Son, Seira Hidano, Kazuhide Fukushima et al.

Adversarial attacks reveal serious flaws in deep learning models. More dangerously, these attacks preserve the original meaning and escape human recognition. Existing methods for detecting these attacks need to be trained using original/adversarial data. In this paper, we propose detection without training by voting on hard labels from predictions of transformations, namely, VoteTRANS. Specifically, VoteTRANS detects adversarial text by comparing the hard labels of input text and its transformation. The evaluation demonstrates that VoteTRANS effectively detects adversarial text across various state-of-the-art attacks, models, and datasets.

Hiroya Kato, Kento Hasegawa, Seira Hidano et al.

Graph Contrastive Learning (GCL) is unsupervised graph representation learning that can obtain useful representation of unknown nodes. The node representation can be utilized as features of downstream tasks. However, GCL is vulnerable to poisoning attacks as with existing learning models. A state-of-the-art defense cannot sufficiently negate adverse effects by poisoned graphs although such a defense introduces adversarial training in the GCL. To achieve further improvement, pruning adversarial edges is important. To the best of our knowledge, the feasibility remains unexplored in the GCL domain. In this paper, we propose a simple defense for GCL, EdgePruner. We focus on the fact that the state-of-the-art poisoning attack on GCL tends to mainly add adversarial edges to create poisoned graphs, which means that pruning edges is important to sanitize the graphs. Thus, EdgePruner prunes edges that contribute to minimizing the contrastive loss based on the node representation obtained after training on poisoned graphs by GCL. Furthermore, we focus on the fact that nodes with distinct features are connected by adversarial edges in poisoned graphs. Thus, we introduce feature similarity between neighboring nodes to help more appropriately determine adversarial edges. This similarity is helpful in further eliminating adverse effects from poisoned graphs on various datasets. Finally, EdgePruner outputs a graph that yields the minimum contrastive loss as the sanitized graph. Our results demonstrate that pruning adversarial edges is feasible on six datasets. EdgePruner can improve the accuracy of node classification under the attack by up to 5.55% compared with that of the state-of-the-art defense. Moreover, we show that EdgePruner is immune to an adaptive attack.

Kento Hasegawa, Kazuki Yamashita, Seira Hidano et al.

In the fourth industrial revolution, securing the protection of the supply chain has become an ever-growing concern. One such cyber threat is a hardware Trojan (HT), a malicious modification to an IC. HTs are often identified in the hardware manufacturing process, but should be removed earlier, when the design is being specified. Machine learning-based HT detection in gate-level netlists is an efficient approach to identify HTs at the early stage. However, feature-based modeling has limitations in discovering an appropriate set of HT features. We thus propose NHTD-GL in this paper, a novel node-wise HT detection method based on graph learning (GL). Given the formal analysis of HT features obtained from domain knowledge, NHTD-GL bridges the gap between graph representation learning and feature-based HT detection. The experimental results demonstrate that NHTD-GL achieves 0.998 detection accuracy and outperforms state-of-the-art node-wise HT detection methods. NHTD-GL extracts HT features without heuristic feature engineering.

Hoang-Quoc Nguyen-Son, Seira Hidano, Kazuhide Fukushima et al.

There are two cases describing how a classifier processes input text, namely, misclassification and correct classification. In terms of misclassified texts, a classifier handles the texts with both incorrect predictions and adversarial texts, which are generated to fool the classifier, which is called a victim. Both types are misunderstood by the victim, but they can still be recognized by other classifiers. This induces large gaps in predicted probabilities between the victim and the other classifiers. In contrast, text correctly classified by the victim is often successfully predicted by the others and induces small gaps. In this paper, we propose an ensemble model based on similarity estimation of predicted probabilities (SEPP) to exploit the large gaps in the misclassified predictions in contrast to small gaps in the correct classification. SEPP then corrects the incorrect predictions of the misclassified texts. We demonstrate the resilience of SEPP in defending and detecting adversarial texts through different types of victim classifiers, classification tasks, and adversarial attacks.

Huy Quoc Le, Dung Hoang Duong, Partha Sarathi Roy et al.

A signcryption, which is an integration of a public key encryption and a digital signature, can provide confidentiality and authenticity simultaneously. Additionally, a signcryption associated with equality test allows a third party (e.g., a cloud server) to check whether or not two ciphertexts are encrypted from the same message without knowing the message. This application plays an important role especially in computing on encrypted data. In this paper, we propose the first lattice-based signcryption scheme equipped with a solution to testing the message equality in the standard model. The proposed signcryption scheme is proven to be secure against insider attacks under the learning with errors assumption and the intractability of the short integer solution problem. As a by-product, we also show that some existing lattice-based signcryptions either is insecure or does not work correctly.

Dung Hoang Duong, Kazuhide Fukushima, Shinsaku Kiyomoto et al.

Public key encryption with equality test (PKEET) supports to check whether two ciphertexts encrypted under different public keys contain the same message or not. PKEET has many interesting applications such as keyword search on encrypted data, encrypted data partitioning for efficient encrypted data management, personal health record systems, spam filtering in encrypted email systems and so on. However, the PKEET scheme lacks an authorization mechanism for a user to control the comparison of its ciphertexts with others. In 2015, Ma et al. introduce the notion of PKEET with flexible authorization (PKEET-FA) which strengthens privacy protection. Since 2015, there are several follow-up works on PKEET-FA. But, all are secure in the random-oracle model. Moreover, all are vulnerable to quantum attacks. In this paper, we provide three constructions of quantum-safe PKEET-FA secure in the standard model. Proposed constructions are secure based on the hardness assumptions of integer lattices and ideal lattices. Finally, we implement the PKEET-FA scheme over ideal lattices.

Dung Hoang Duong, Partha Sarathi Roy, Willy Susilo et al.

With the rapid growth of cloud storage and cloud computing services, many organisations and users choose to store the data on a cloud server for saving costs. However, due to security concerns, data of users would be encrypted before sending to the cloud. However, this hinders a problem of computation on encrypted data in the cloud, especially in the case of performing data matching in various medical scenarios. Public key encryption with equality test (PKEET) is a powerful tool that allows the authorized cloud server to check whether two ciphertexts are generated by the same message. PKEET has then become a promising candidate for many practical applications like efficient data management on encrypted databases. Lee et al. (Information Sciences 2020) proposed a generic construction of PKEET schemes in the standard model and hence it is possible to yield the first instantiation of post-quantum PKEET schemes based on lattices. At ACISP 2019, Duong et al. proposed a direct construction of PKEET over integer lattices in the standard model. However, their scheme does not reach the CCA2-security. In this paper, we propose an efficient CCA2-secure PKEET scheme based on ideal lattices. In addition, we present a modification of the scheme by Duong et al. over integer lattices to attain the CCA2-security. Both schemes are proven secure in the standard model, and they enjoy the security in the upcoming quantum computer era.