Reviewerly/exHarmony huggingface dataset:
BioLeak: Exploiting Cache Timing to Recover Fingerprint Minutiae Coordinates

The wide deployment of biometric authentication and particularly fingerprint matching on mobile devices and laptops raises the question about their security. While respective algorithms have been extensively analysed regarding their ability to correctly identify a specific individual (and reject others), little attention has been paid to their secure implementation, especially on multi-user and multi-process systems. In this paper, we focus on this aspect and show that cache attacks on real-world biometric algorithms are a viable way to extract the user's fingerprint minutiae coordinates using a single side-channel trace. Specifically, we analyse NIST's MindTCT library that is used by the Linux fprintd fingerprint authentication system to identify suitable addresses for a Flush+Reload attack, then devise post-processing techniques to recover minutiae information. Using 1000 synthetic test fingerprints, our method succeeds in approximately 9% of cases to recover minutiae from a single cache trace. Our work proves that there is side-channel leakage from a widely used biometric algorithm and therefore more research should be performed on hardening biometric algorithms against such attacks.
Case Selection Logic, Operationalization, and Robustness

Subject International Relations Collection: Oxford Scholarship Online
Better Side-Channel Attacks Through Measurements

In recent years, there has been a growing focus on improving the efficiency of the power side-channel analysis (SCA) attack by using machine learning or artificial intelligence methods, however, they can only be as good as the data they are trained on. Previous work has not given much attention to improving the accuracy of measurements by optimizing the measurement setup and the parameters, and most new researchers rely on heuristics to make measurements. This paper proposes an effective methodology to launch power SCA and increase the efficiency of the attack by improving the measurements. We examine the heuristics related to measurement parameters, investigate ways to optimize the parameters, determine their effects empirically, and provide a theoretical analysis to support the findings. To demonstrate the shortcomings of commercial measurement devices, we present a low-cost measurement board design and its hardware realization. In doing so, we are able to improve the power measurements, by optimizing the measurement setup, which in turn improves the efficiency of the attack.
Remote Fault Injection Attack against Cryptographic Modules via Intentional Electromagnetic Interference from an Antenna

Fault injection attacks on cryptographic modules pose significant threats, yet conventional fault injection methods require physical access to the target device. This paper introduces a novel fault injection method using Intentional Electromagnetic Interference (IEMI) to induce temporary faults in cryptographic modules without intrusion, proximity, or synchronization with the encryption process. The proposed method selects a frequency that can cause faults only in the target cryptographic modules without disrupting other modules in the device. Additionally, faults suitable for secret key analysis are efficiently generated even when EM waves are injected asynchronously into the cryptographic operation. To demonstrate the effectiveness of the proposed method, an experiment was conducted where EM waves were irradiated from an antenna positioned 2 meters away from a cryptographic device with an Advanced Encryption Standard (AES) implementation, inducing faults. The secret key was successfully retrieved by applying Differential Fault Analysis (DFA) to the obtained faulty ciphertexts. The proposed method holds the potential to be applied to devices that have previously been considered outside the scope of fault injection attack threats, owing to the difficulty in implementing conventional fault scenarios. This suggests a broader range of applicability for addressing security concerns in such devices. Consequently, there exists a possibility that even devices already in circulation could become susceptible to these threats, necessitating the implementation of measures to protect such equipment against potential attacks. In the countermeasure approach against this kind of threat, we propose and demonstrate the ability to significantly reduce the transmission efficiency of EM waves utilized for attacks by expanding upon the concept of EM shielding. Consequently, it significantly decreased the occurrence rate of faults. This is achieved by merely positioning conductive materials close to cryptographic devices rather than completely enclosing them in a conductive enclosure.
Now is the Time: Scalable and Cloud-supported Audio Conferencing using End-to-End Homomorphic Encryption

Homomorphic encryption (HE) allows computations on encrypted data, leaking neither the input nor the computational output. While the method has historically been infeasible to use in practice, due to recent advancements, HE has started to be applied in real-world applications. Motivated by the possibility of outsourcing heavy computations to the cloud and still maintaining end-to-end security, in this paper, we use HE to design a basic audio conferencing application and demonstrate that our design approach (including some advanced features) is both practical and scalable. First, by homomorphically mixing encrypted audio in an untrusted, honest-but-curious server, we demonstrate the practical use of HE in audio communication. Second, by using multiplication operations, we go beyond the purely additive audio mixing and implement advanced example features capable of handling server-side mute and breakout rooms without the cloud server being able to extract sensitive user-specific metadata. Whereas the encryption and decryption times are shown to be magnitudes slower than generic AES encryption and roughly ten times slower than Signal's AES implementation, our solution approach is scalable and achieves end-to-end encryption while keeping performance well within the bounds of practical use. Third, besides studying the performance aspects, we also objectively evaluate the perceived audio quality, demonstrating that this approach also achieves excellent audio quality. Finally, our comprehensive evaluation and empirical findings provide valuable insights into the tradeoffs between HE schemes, their security configurations, and audio parameters. Combined, our results demonstrate that audio mixing using HE (including advanced features) now can be made both practical and scalable.
Optimizing 0-RTT Key Exchange with Full Forward Security

Secure communication protocols such as TLS 1.3 or QUIC are doing the heavy lifting in terms of security of today's Internet. These modern protocols provide modes that do not need an interactive handshake, but allow to send cryptographically protected data with the first client message in zero round-trip time (0-RTT). While this helps to reduce communication latency, the security of such protocols in terms of forward security is rather weak.
Enterprise Cyber Threat Modeling and Simulation of Loss Events for Cyber Risk Quantification

In today's enterprise landscape, effective risk management has emerged as a vital cornerstone. This importance has escalated significantly due to the widespread transition from traditional on-premise infrastructures to dynamic cloud environments. Many organizations rely on qualitative approaches for internal IT and cyber risk management; however, these approaches have notable drawbacks, such as a lack of accuracy and comparability. In this paper, we propose a novel approach to address these limitations by using the Factor Analysis of Information Risk (FAIR) methodology in conjunction with MITRE ATT&CK to model realistic cyberattacks on organizations and measure quantitative risk. We describe how this approach can be used to create an enterprise cyber threat model, providing a case study for a cloud scenario to demonstrate its usage and to illustrate its potential benefits.
Too Close for Comfort? Measuring Success of Sampled-Data Leakage Attacks Against Encrypted Search

The well-defined information leakage of Encrypted Search Algorithms (ESAs) is predominantly analyzed by crafting so-called leakage attacks. These attacks utilize adversarially known auxiliary data and the observed leakage to attack an ESA instance built on a user's data. Known-data attacks require the auxiliary data to be a subset of the user's data. In contrast, sampled-data attacks merely rely on auxiliary data that is, in some sense, statistically close to the user's data and hence reflect a much more realistic attack scenario where the auxiliary data stems from a publicly available data source instead of the private user's data.
Synthesis of Optimal Control of Innovative Projects of an Industrial Enterprise: Adaptability and Stability

The development of innovations in a disturbed business environment encounters the difficulties of incomplete a priori information, which worsens the conditions for achieving adaptability and stability of industrial enterprises. Carrying out the synthesis of the optimal control of innovative projects based on the formulation and analysis of objective functions that reflect the investment support of innovations and the desire to increase the production of innovative products indicates the profitability of the interaction of the parties involved in the innovation process. With the exchange of information between them, it is possible to combine the opposing interests of the parties and achieve a preferable compromise solution that improves the overall situation with the development of an innovative project and thereby contributes to an increase in the adaptability and stability of industrial enterprises.
A Decoder Structure Guided CNN‐Transformer Network for face super‐resolution

Abstract Recent advances in deep convolutional neural networks have shown improved performance in face super‐resolution through joint training with other tasks such as face analysis and landmark prediction. However, these methods have certain limitations. One major limitation is the requirement for manual marking information on the dataset for multi‐task joint learning. This additional marking process increases the computational cost of the network model. Additionally, since prior information is often estimated from low‐quality faces, the obtained guidance information tends to be inaccurate. To address these challenges, a novel Decoder Structure Guided CNN‐Transformer Network (DCTNet) is introduced, which utilises the newly proposed Global‐Local Feature Extraction Unit (GLFEU) for effective embedding. Specifically, the proposed GLFEU is composed of an attention branch and a Transformer branch, to simultaneously restore global facial structure and local texture details. Additionally, a Multi‐Stage Feature Fusion Module is incorporated to fuse features from different network stages, further improving the quality of the restored face images. Compared with previous methods, DCTNet improves Peak Signal‐to‐Noise Ratio by 0.23 and 0.19 dB on the CelebA and Helen datasets, respectively. Experimental results demonstrate that the designed DCTNet offers a simple yet powerful solution to recover detailed facial structures from low‐quality images.
Spatially‐correlated time series clustering using location‐dependent Dirichlet process mixture model

Abstract The Dirichlet process mixture (DPM) model has been widely used as a Bayesian nonparametric model for clustering. However, the exchangeability assumption of the Dirichlet process is not valid for clustering spatially correlated time series as these data are indexed spatially and temporally. While analyzing spatially correlated time series, correlations between observations at proximal times and locations must be appropriately considered. In this study, we propose a location‐dependent DPM model by extending the traditional DPM model for clustering spatially correlated time series. We model the temporal pattern as an infinite mixture of Gaussian processes while considering spatial dependency using a location‐dependent Dirichlet process prior over mixture components. This encourages the assignment of observations from proximal locations to the same cluster. By contrast, because mixture atoms for modeling temporal patterns are shared across space, observations with similar temporal patterns can be still grouped together even if they are located far apart. The proposed model also allows the number of clusters to be automatically determined in the clustering procedure. We validate the proposed model using simulated examples. Moreover, in a real case study, we cluster adjacent roads based on their traffic speed patterns that have changed as a result of a traffic accident occurred in Seoul, South Korea.
Modeling subpopulations for hierarchically structured data

Abstract The field of forensic statistics offers a unique hierarchical data structure in which a population is composed of several subpopulations of sources and a sample is collected from each source. This subpopulation structure creates an additional layer of complexity. Hence, the data has a hierarchical structure in addition to the existence of underlying subpopulations. Finite mixtures are known for modeling heterogeneity; however, previous parameter estimation procedures assume that the data is generated through a simple random sampling process. We propose using a semi‐supervised mixture modeling approach to model the subpopulation structure which leverages the fact that we know the collection of samples came from the same source, yet an unknown subpopulation. A simulation study and a real data analysis based on famous glass datasets and a keystroke dynamic typing data set show that the proposed approach performs better than other approaches that have been used previously in practice.
Enhancing text classification with attention matrices based on BERT

Summary Text classification is a critical task in the field of natural language processing. While pre‐trained language models like BERT have made significant strides in improving performance in this area, the distinctive dependency information that is present in text has not been fully exploited. Besides, BERT mostly captures phrase‐level information in lower layers, which becomes progressively weaker with the increasing depth of layers. To address these limitations, our work focuses on enhancing text classification through the incorporation of Attention Matrices, particularly in the fine‐tuning process of pre‐trained models like BERT. Our approach, named AM‐BERT, leverages learned dependency relationships as external knowledge to enhance the pre‐trained model by generating attention matrices. In addition, we introduce a new learning strategy that enables the model to retain learned phrase‐level structure information. Extensive experiments and detailed analysis on multiple benchmark datasets demonstrate the effectiveness of our approach in text classification tasks. Furthermore, we show that AM‐BERT achieves stable performance improvements also in named entity recognition tasks.
Retranslated Chinese classical canon <i>Journey to the West</i>: a stylometric comparison between Julia Lovell’s retranslation and Arthur Waley’s translation

Abstract Journey to the West (西游记; Xi You Ji) is one of the best-known classic novels in Chinese literature, and it has been translated into English by various notable translators. This study compares the retranslation of the novel by Julia Lovell published in “Penguin Classics” in 2021 with Arthur Waley’s translation in “Penguin Classics” in 1973. Employing a stylometric approach to compare the styles of the two translations, it uses the corpus tools of word lists, keyword analysis, and function word analysis. The results reveal distinct stylistic differences in the translators’ styles, including lexical density, sentence length, and function words, which point to different translation strategies employed by the two translations of this classic Chinese novel. The book reviews published so far indicate that Lovell’s version has been well received by contemporary Anglophone readers and that the changes in this retranslation may have helped to explain the recanonization of the original classic. This study provides new insights into the understanding of the differences between translation and retranslation, and thus has implications for the fields of translator style studies and stylometry research.
Exploring Human Capital's Role in Driving Sustainable Organizational Development in the Era of the Internet of Things

The Internet of Things (IoT), a network of interconnected devices and sensors linked through the Internet, facilitates the connection between individuals, their devices, and vast amounts of data. Within companies, the human resources department plays a crucial role in managing the pressure generated by various aspects, including marketing, development, and the application of IoT in human resources. This study is motivated by the need to understand the potential of IoT in strategic human resource management practices. To explore the fundamental strategic human resource management practices and assess their mediating effects, this research gathered data from 320 respondents in the Kerala information technology sector, specifically Kochi Infopark, consisting of both lower-level executives and senior managers. The study utilizes statistical analysis to examine the relationship between sustainable human resource practices, the Internet of Things, and sustainable human resource growth. Furthermore, the study highlights the IoT's role as a mediator between strategic human resource management practices and factors contributing to sustainable HR growth, demonstrating a positive impact of independent variables on dependent variables. These findings emphasize the potential for organizations to enhance HR practices, decision-making, and innovation using IoT during crises and uncertainties. Integrating IoT with SHRM empowers organizations to effectively manage challenges and complex environments.
The antecedents of employees' proactive information security behaviour: The perspective of proactive motivation

Abstract Organisational information security (ISec) protection is undergoing a turbulent shift in the workplace environment. In an environment of ever‐increasing risks of insider threats and external cyberattacks, individual employees are often expected to take the initiative to solve organisational security problems. This study therefore focuses on employees' proactive information security behaviours (ISBs)—behaviours that are self‐initiated, change‐oriented, and future‐focused—and the motivations that compel employees to protect organisational assets. We ground our study in Parker et al. (2010) proactive motivation theory (ProMT) and develop an integrated multilevel model to examine the respective effects of proactive motivational states, that is, can‐do , reason‐to , and energised‐to motivations, on employees' proactive ISBs. We also explore the roles of individual differences and contextual factors—namely, proactive personality and supervisory ISec support—and their influences on proactive motivational states. Data were collected from 210 employees situated in 55 departments distributed among multiple organisations located in China. The results show that supervisory ISec support positively influences employees' proactive motivational states and thereby boosts employees' proactive ISBs. Proactive personality negatively moderates the effect of supervisory ISec support on flexible security role orientation (reason‐to motivation). By identifying the antecedents of employees' proactive ISBs, we make key theoretical contributions to ISec research and valuable practical contributions to organisational ISec management.
Cognitive Impact of Social Virtual Reality: Audience and Mere Presence Effect of Virtual Companions

Sharing experiences with others is an important part of everyday life. Immersive virtual reality (IVR) promises to simulate these experiences. However, whether IVR elicits a similar level of social presence as measured in the real world is unclear. It is also uncertain whether AI-driven virtual humans (agents) can elicit a similar level of meaningful social copresence as people-driven virtual-humans (avatars). The current study demonstrates that both virtual human types can elicit a cognitive impact on a social partner. The current experiment tested participants’ cognitive performance changes in the presence of virtual social partners by measuring the social facilitation effect (SFE). The SFE-related performance change can occur through either vigilance-based mechanisms related to other people’s copresence (known as the mere presence effect (MPE)) or reputation management mechanisms related to other people’s monitoring (the audience effect (AE)). In this study, we hypothesised AE and MPE as distinct mechanisms of eliciting SFE. Firstly, we predicted that, if head-mounted IVR can simulate sufficient copresence, any social companion’s visual presence would elicit SFE through MPE. The results demonstrated that companion presence decreased participants’ performance irrespective of whether AI or human-driven. Secondly, we predicted that monitoring by a human-driven, but not an AI-driven, companion would elicit SFE through AE. The results demonstrated that monitoring by a human-driven companion affected participant performance more than AI-driven, worsening performance marginally in accuracy and significantly in reaction times. We discuss how the current results explain the findings in prior SFE in virtual-world literature and map out future considerations for social-IVR testing, such as participants’ virtual self-presence and affordances of physical and IVR testing environments.
Feasibility Analysis of Cracking RSA with Improved Quantum Circuits of the Shor’s Algorithm

Since the RSA public key cryptosystem was proposed, it has been widely used because of its strong security. Although the proposal of the Shor’s algorithm offers hope for cracking RSA, it is debatable whether the algorithm can actually pose a threat in practice. From the perspective of the quantum circuit of the Shor’s algorithm, we analyse the feasibility of cracking RSA with improved quantum circuits using an ion-trap quantum computer. We present an improved quantum circuit for the modular exponentiation of a constant, which is the most expensive operation in Shor’s algorithm for integer factorization. Whereas previous studies mostly focused on minimizing the number of qubits or the depth of the circuit, we minimize the number of CNOTs, which greatly affects the time to run the algorithm on an ion-trap quantum computer. First, we give the implementation of the basic arithmetic with the lowest known number of CNOTs and the construction of an improved modular exponentiation of a constant by accumulating intermediate data and using a windowing technique. Then, we precisely estimate the number of improved quantum circuits needed to perform the Shor’s algorithm for factoring an <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>n</mi> </math> -bit integer, which is <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mn>217</mn> <msup> <mrow> <mi>n</mi> </mrow> <mrow> <mn>3</mn> </mrow> </msup> <mo>/</mo> <mrow> <msub> <mrow> <mi mathvariant="normal">log</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> <mtext> </mtext> <mi>n</mi> </mrow> <mrow> <mo>+</mo> <mn>4</mn> </mrow> <msup> <mrow> <mi>n</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msup> <mo>+</mo> <mi>n</mi> </math> . We analyse the running time and feasibility of the Shor’s algorithm on an ion-trap quantum computer according to the number of CNOTs. Finally, we discussed the lower bound of the number of CNOTs needed to implement the Shor’s algorithm.