Theme Aspect Argumentation Model for Handling Fallacies

From daily discussions to marketing ads to political statements, information manipulation is rife. It is increasingly more important that we have the right set of tools to defend ourselves from manipulative rhetoric, or fallacies. Suitable techniques to automatically identify fallacies are being investigated in natural language processing research. However, a fallacy in one context may not be a fallacy in another context, so there is also a need to explain how and why it has come to be judged a fallacy. For the explainable fallacy identification, we present a novel approach to characterising fallacies through formal constraints, as a viable alternative to more traditional fallacy classifications by informal criteria. To achieve this objective, we introduce a novel context-aware argumentation model, the theme aspect argumentation model, which can do both: the modelling of a given argumentation as it is expressed (rhetorical modelling); and a deeper semantic analysis of the rhetorical argumentation model. By identifying fallacies with formal constraints, it becomes possible to tell whether a fallacy lurks in the modelled rhetoric with a formal rigour. We present core formal constraints for the theme aspect argumentation model and then more formal constraints that improve its fallacy identification capability. We show and prove the consequences of these formal constraints. We then analyse the computational complexities of deciding the satisfiability of the constraints

Security Analysis of End-to-End Encryption for Zoom Meetings

In the wake of the global COVID-19 pandemic, video conference systems have become essential for not only business purposes, but also private, academic, and educational uses. Among the various systems, Zoom is the most widely deployed video conference system. In October 2020, Zoom Video Communications rolled out their end-to-end encryption (E2EE) to protect conversations in a meeting from even insiders, namely, the service provider Zoom. In this study, we conduct thorough security evaluations of the E2EE of Zoom (version 2.3.1) by analyzing their cryptographic protocols. We discover several attacks more powerful than those expected by Zoom according to their whitepaper. Specifically, if insiders collude with meeting participants, they can impersonate any Zoom user in target meetings, whereas Zoom indicates that they can impersonate only the current meeting participants. Besides, even without relying on malicious participants, insiders can impersonate any Zoom user in target meetings though they cannot decrypt meeting streams. In addition, we demonstrate several impersonation attacks by meeting participants or insiders colluding with meeting participants. Although these attacks may be beyond the scope of the security claims made by Zoom or may be already mentioned in the whitepaper, we reveal the details of the attack procedures and their feasibility in the real-world setting and propose effective countermeasures in this paper. Our findings are not an immediate threat to the E2EE of Zoom; however, we believe that these security evaluations are of value for deeply understanding the security of E2EE of Zoom

Security Analysis of SFrame

As people become more and more privacy conscious, the need for end-to-end encryption (E2EE) has become widely recognized. We study herein the security of SFrame, an E2EE mechanism recently proposed to the Internet Engineering Task Force for video/audio group communications over the Internet. Despite being a quite recent project, SFrame is going to be adopted by a number of real-world applications. We inspect the original specification of SFrame and find critical issues that will lead to impersonation (forgery) attacks with a practical complexity by a malicious group member. We also investigate the several publicly available SFrame implementations and confirm that this issue is present in these implementations

PNB-focused Differential Cryptanalysis of ChaCha Stream Cipher

This study focuses on differential cryptanalysis of the ChaCha stream cipher. In the conventional approach, an adversary first searches for an input/output differential pair with the highest differential bias and then analyzes the probabilistic neutral bits (PNB) based on the obtained input/output differential pair. However, although the time and data complexities for the attack can be estimated by the differential bias and PNB obtained by this approach, the combination of the differential bias and PNB is not always optimal. In addition, the existing studies have not performed a comprehensive analysis of the PNB; thus, they have not provided an upper bound on the number of rounds required for a differential attack that uses a single-bit truncated differential to be successful. To address these limitations, we propose a PNB-focused differential attack on reduced-round ChaCha by first comprehensively analyzing the PNB for all possible single-bit truncated output differences and then searching for the input/output differential pair with the highest differential bias based on the obtained PNB. The best existing attack on ChaCha, proposed by Beierle et al. at CRYPTO 2020, works on up to 7 rounds, whereas the most extended attack we observed works on up to 7.25 rounds using the proposed PNB-focused approach. The time complexity, data complexity, and success probability of the proposed attack are $2^{255.62}$, $2^{48.36}$, and 0.5, respectively. Although the proposed attack is less efficient than a brute force attack, it is the first dedicated attack on the target and provides both a baseline and useful components (i.e., differential bias and PNB) for improved attacks

Parallel SAT Framework to Find Clustering of Differential Characteristics and Its Applications

The most crucial but time-consuming task for differential cryptanalysis is to find a differential with a high probability. To tackle this task, we propose a new SAT-based automatic search framework to efficiently figure out a differential with the highest probability under a specified condition. As the previous SAT methods (e.g., the Sun et al’s method proposed at ToSC 2021(1)) focused on accelerating the search for an optimal single differential characteristic, these are not optimized for evaluating a clustering effect to obtain a tighter differential probability of differentials. In contrast, our framework takes advantage of a method to solve incremental SAT problems in parallel using a multi-threading technique, and consequently, it offers the following advantages compared with the previous methods: (1) speedy identification of a differential with the highest probability under the specified conditions; (2) efficient construction of the truncated differential with the highest probability from the obtained multiple differentials; and (3) applicability to a wide class of symmetric-key primitives. To demonstrate the effectiveness of our framework, we apply it to the block cipher PRINCE and the tweakable block cipher QARMA. We successfully figure out the tight differential bounds for all variants of PRINCE and QARMA within the practical time, thereby identifying the longest distinguisher for all the variants, which improves existing ones by one to four more rounds. Besides, we uncover notable differences between PRINCE and QARMA in the behavior of differential, especially for the clustering effect. We believe that our findings shed light on new structural properties of these important primitives. In the context of key recovery attacks, our framework allows us to derive the key-recovery-friendly truncated differentials for all variants of QARMA. Consequently, we report key recovery attacks based on (truncated) differential cryptanalysis on QARMA for the first time and show these key recovery attacks are competitive with existing other attacks

State-free End-to-End Encrypted Storage and Chat Systems based on Searchable Encryption

Searchable symmetric encryption (SSE) has attracted significant attention because it can prevent data leakage from external devices, e.g., clouds. SSE appears to be effective to construct such a secure system; however, it is not trivial to construct such a system from SSE in practice because other parts must be designed, e.g., user login management, defining the keyword space, and sharing secret keys among multiple users who usually do not have public key certificates. In this paper, we describe the implementation of two systems based upon the state-free dynamic SSE (DSSE) (Watanabe et al., IEICE Transactions 2022), i.e., a secure storage system (for a single user) and a chat system (for multiple users). In addition to the DSSE protocol, we employ a secure multipath key exchange (SMKEX) protocol (Costea et al., CCS 2018), which is secure against some classes of unsynchronized active attackers. It allows the chat system users without certificates to share a secret key of the DSSE protocol in a secure manner. To realize end-to-end encryption, the shared key must be kept secret; thus, we must consider how to preserve the secret on, for example, a user\u27s local device. However, this requires additional security assumptions, e.g., tamper resistance, and it seems difficult to assume that all users have such devices. Thus, we propose a secure key agreement protocol by employing the SMKEX and login information (password) that does not require an additional tamper-resistant device. Combining the proposed key agreement protocol with the underlying state-free DSSE protocol allow users who know the password to use the systems from multiple devices. We also consider a kind of explainability of the system. That is, usually, general users are not aware of the underlying DSSE and thus such secure systems should be used without recognizing the underlying cryptographic tools. On the other hand, it is highly desirable to easily explain how to encrypt data, how to preserve encrypted data on external storages, and so on, even for general users. Thus, we also implement a concierge functionality that visualizes DSSE-related data processing

Bit-wise Cryptanalysis on AND-RX Permutation Friet-PC

This paper presents three attack vectors of bit-wise cryptanalysis including rotational, bit-wise differential, and zero-sum distinguishing attacks on the AND-RX permutation Friet-PC, which is implemented in a lightweight authenticated encryption scheme Friet. First, we propose a generic procedure for a rotational attack on AND-RX cipher with round constants. By applying the proposed attack to Friet-PC, we can construct an 8-round rotational distinguisher with a time complexity of 2^{102}. Next, we explore single- and dual-bit differential biases, which are inspired by the existing study on Salsa and ChaCha, and observe the best bit-wise differential bias with 2^{−9.552}. This bias allows us to practically construct a 9-round bit-wise differential distinguisher with a time complexity of 2^{20.044}. Finally, we construct 13-, 15-, 17-, and 30-round zero-sum distinguishers with time complexities of 2^{31}, 2^{63}, 2^{127}, and 2^{383}, respectively. To summarize our study, we apply three attack vectors of bit-wise cryptanalysis to Friet-PC and show their superiority as effective attacks on AND-RX ciphers

Distinguishing and Key Recovery Attacks on the Reduced-Round SNOW-V and SNOW-Vi

This paper presents distinguishing and key recovery attacks on the reduced-round SNOW-V and SNOW-Vi, which are stream ciphers proposed for standard encryption schemes for the 5G mobile communication system. First, we construct a Mixed-Integer Linear Programming (MILP) model to search for integral characteristics using the division property, and find the best integral distinguisher in the 3-, 4-, 5-round SNOW-V, and 5-round SNOW-Vi with time complexities of $2^{8}$, $2^{16}$, $2^{48}$, and $2^{16}$, respectively. Next, we construct a bit-level MILP model to efficiently search for differential characteristics, and find the best differential characteristics in the 3- and 4-round versions. These characteristics lead to the 3-round differential distinguishers for SNOW-V and SNOW-Vi with time complexities of $2^{17}$ and $2^{12}$ and the 4-round differential distinguishers for SNOW-V and SNOW-Vi with time complexities of $2^{97}$ and $2^{39}$, respectively. Then, we consider single-bit and dual-bit differential cryptanalysis, which is inspired by the existing study on Salsa and ChaCha. By carefully choosing the IV values and differences, we can construct practical bit-wise differential distinguishers for the 4-round SNOW-V, 4-, and 5-round SNOW-Vi with time complexities of $2^{4.466}$, $2^{1.000}$, and $2^{14.670}$, respectively. Finally, we improve the existing differential attack based on probabilistic neutral bits, which is also inspired by the existing study on Salsa and ChaCha. As a result, we present the best key recovery attack on the 4-round SNOW-V and SNOW-Vi with time complexities of $2^{153.97}$ and $2^{233.99}$ and data complexities of $2^{26.96}$ and $2^{19.19}$, respectively. Consequently, we significantly improve the existing best attacks in the initialization phase by the designers

Output Prediction Attacks on Block Ciphers using Deep Learning

Cryptanalysis of symmetric-key ciphers, e.g., linear/differential cryptanalysis, requires an adversary to know the internal structures of the target ciphers. On the other hand, deep learning-based cryptanalysis has attracted significant attention because the adversary is not assumed to have knowledge about the target ciphers with the exception of the algorithm interfaces. Such cryptanalysis in a blackbox setting is extremely strong; thus, we must design symmetric-key ciphers that are secure against deep learning-based cryptanalysis. However, almost previous attacks do not clarify what features or internal structures affect success probabilities. Although Benamira et al. (Eurocrypt 2021) and Chen et al. (ePrint 2021) analyzed Gohr’s results (CRYPTO 2019), they did not find any deep learning specific characteristic where it affects the success probabilities of deep learning-based attacks but does not affect those of linear/differential cryptanalysis. Therefore, it is difficult to employ the results of such cryptanalysis to design deep learning-resistant symmetric-key ciphers. In this paper, we propose deep learning-based output prediction attacks in a blackbox setting. As preliminary experiments, we first focus on two toy SPN block ciphers (small PRESENT-[4] and small AES-[4]) and one toy Feistel block cipher (small TWINE-[4]). Due to its small internal structures with a block size of 16 bits, we can construct deep learning models by employing the maximum number of plaintext/ciphertext pairs, and we can precisely calculate the rounds in which full diffusion occurs. Next, based on the preliminary experiments, we explore whether the evaluation results obtained by our attacks against three toy block ciphers can be applied to block ciphers with large block sizes, e.g., 32 and 64 bits. As a result, we demonstrate the following results, specifically for the SPN block ciphers: First, our attacks work against a similar number of rounds that the linear/differential attacks can be successful. Next, our attacks realize output predictions (precisely ciphertext prediction and plaintext recovery) that are much stronger than distinguishing attacks. Then, swapping or replacing the internal components of the target block ciphers affects the average success probabilities of the proposed attacks. It is particularly worth noting that this is a deep learning specific characteristic because swapping/replacing does not affect the average success probabilities of the linear/differential attacks. Finally, by analyzing the influence of the differences in the characteristics of three S-boxes (i.e., the original PRESENT S-box and two known weak S-boxes) on deep learning specific characteristics, we clarify that the resistance of the target ciphers to differential/linear attacks can affect the success probability of deep learning-based attacks. We also confirm whether the proposed attacks work on the Feistel block cipher. We expect that our results will be an important stepping stone in the design of deep learning-resistant symmetric-key ciphers

Areion: Highly-Efficient Permutations and Its Applications (Extended Version)

In real-world applications, the overwhelming majority of cases require (authenticated) encryption or hashing with relatively short input, say up to 2K bytes. Almost all TCP/IP packets are 40 to 1.5K bytes, and the maximum packet lengths of major protocols, e.g., Zigbee, Bluetooth low energy, and Controller Area Network (CAN), are less than 128 bytes. However, existing schemes are not well optimized for short input. To bridge the gap between real-world needs (in the future) and limited performances of state-of-the-art hash functions and authenticated encryptions with associated data (AEADs) for short input, we design a family of wide-block permutations Areion that fully leverages the power of AES instructions, which are widely deployed in many devices. As for its applications, we propose several hash functions and AEADs. Areion significantly outperforms existing schemes for short input and even competitive to relatively long messages. Indeed, our hash function is surprisingly fast, and its performance is less than three cycles/byte in the latest Intel architecture for any message size. It is significantly much faster than existing state-of-the-art schemes for short messages up to around 100 bytes, which are the most widely-used input size in real-world applications, on both the latest CPU architectures (IceLake, Tiger Lake, and Alder Lake) and mobile platforms (Pixel 7, iPhone 14, and iPad Pro with Apple M2)