Reviewerly/exHarmony huggingface dataset:
FLAIR: Defense against Model Poisoning Attack in Federated Learning

Federated learning—multi-party, distributed learning in a decentralized environment—is vulnerable to model poisoning attacks, more so than centralized learning. This is because malicious clients can collude and send in carefully tailored model updates to make the global model inaccurate. This motivated the development of Byzantine-resilient federated learning algorithms, such as Krum, Bulyan, FABA, and FoolsGold. However, a recently developed untargeted model poisoning attack showed that all prior defenses can be bypassed. The attack uses the intuition that simply by changing the sign of the gradient updates that the optimizer is computing, for a set of malicious clients, a model can be diverted from the optima to increase the test error rate. In this work, we develop FLAIR—a defense against this directed deviation attack (DDA), a state-of-the-art model poisoning attack. FLAIR is based on our intuition that in federated learning, certain patterns of gradient flips are indicative of an attack. This intuition is remarkably stable across different learning algorithms, models, and datasets. FLAIR assigns reputation scores to the participating clients based on their behavior during the training phase and then takes a weighted contribution of the clients. We show that where the existing defense baselines of FABA [IJCAI '19], FoolsGold [Usenix '20], and FLTrust [NDSS '21] fail when 20-30% of the clients are malicious, FLAIR provides byzantine-robustness upto a malicious client percentage of 45%. We also show that FLAIR provides robustness against even a white-box version of DDA.
QUDA: Query-Limited Data-Free Model Extraction

Model extraction attack typically refers to extracting non-public information from a black-box machine learning model. Its unauthorized nature poses significant threat to intellectual property rights of the model owners. By using the well-designed queries and the predictions returned from the victim model, the adversary is able to train a clone model from scratch to obtain similar functionality as victim model. Recently, some methods have been proposed to perform model extraction attacks without using any in-distribution data (Data-free setting). Although these methods have been shown to achieve high clone accuracy, their query budgets are typically around 10 million or even exceed 20 million in some datasets, which lead to a high cost of model stealing and can be easily defended by limiting the number of queries. To illustrate the severe threats induced by model extraction attacks with limited query budget in realistic scenarios, we propose QUDA – a novel QUey-limited DAta-free model extraction attack that incorporates GAN pre-trained by public unrelated dataset to provide weak image prior and the technique of deep reinforcement learning to make query generation strategy more efficient. Compared with the state-of-the-art data-free model extraction method, QUDA achieves better results under query-limited condition (0.1M query budget) in FMNIST and CIFAR-10 datasets, and even outperforms the baseline method in most cases when QUDA uses only 10% query budget of its. QUDA issued a warning that solely relying on the limited numbers of queries or the confidentiality of training data is not reliable to protect model’s security and privacy. Potential countermeasures, such as detection-based defense approach, are also provided.
Eliminating Vulnerabilities by Disabling Unwanted Functionality in Binary Programs

Driven by application diversification and market needs, software systems are integrating new features rapidly. However, this “feature creep” can compromise software security, as more code carries the risk of more vulnerabilities. This paper presents a system for disabling features activated by common input types, using a component called F-detector to detect feature-associated program control flow branches. The system includes a second component called F-blocker to disable features without disrupting application continuity. It does so by treating unwanted features as unexpected errors and leveraging error virtualization to recover execution, by redirecting it to appropriate existing error handling code. We implemented and evaluated the system on the Linux platform using 145 features from 9 programs, and results show that it can detect and disable all features with few errors, hence, outperforming previous works in terms of vulnerability mitigation through debloating.
A Scalable Double Oracle Algorithm for Hardening Large Active Directory Systems

Active Directory (AD) is a popular information security management system for Windows domain networks and is an ongoing common target for cyber attacks. Most real-world Active Directory systems consist of millions of entities and links, and there are currently no efficient and effective solutions for hardening Active Directory systems of such scale. In this paper, we propose a novel and scalable double oracle-based algorithm for hardening large AD systems. We formulate the problem as a Stackelberg game between the defender and the attacker on a weighted AD attack graph, where the defender acts as the leader with a budget, and the objective is to find an optimal defender’s pure strategy. We show that our double oracle-based solution has significantly improved speed and scalability compared with previous solutions for hardening AD systems. Lastly, we compare with GoodHound weakest links and show that our solution provides better recommendations for targeting the elimination of optimal attack paths.
SPEAR-V: Secure and Practical Enclave Architecture for RISC-V

Trusted Execution Environments (TEEs) and enclaves have become increasingly popular and are used from embedded devices to cloud servers. Today, many enclave architectures exist for different ISAs. However, some suffer from performance issues and controlled-channel attacks, while others only support constrained use cases for embedded devices or impose unrealistic constraints on the software. Modern cloud applications require a more flexible architecture that is both secure against such attacks and not constrained by, e.g., a limited number of physical memory ranges.
ShowTime: Amplifying Arbitrary CPU Timing Side Channels

Microarchitectural attacks typically rely on precise timing sources to uncover short-lived secret-dependent activity in the processor. In response, many browsers and even CPU vendors restrict access to fine-grained timers. While some attacks are still possible, several state-of-the-art microarchitectural attack vectors are actively hindered or even eliminated by these restrictions.
Communication-Efficient Inner Product Private Join and Compute with Cardinality

Private join and compute (PJC) is a paradigm where two parties owing their private database securely join their databases and compute a function over the combined database. Inner product PJC, introduced by Lepoint et al. (Asiacrypt’21), is a class of PJC that has a wide range of applications such as secure analysis of advertising campaigns. In this computation, two parties, each of which has a set of identifier-value pairs, compute the inner product of the values after the (inner) join of their databases with respect to the identifiers. They proposed inner product PJC protocols that are specialized for the unbalanced setting where the input sizes of both parties are significantly different and not suitable for the balanced setting where the sizes of two inputs are relatively close.
DHBE: Data-free Holistic Backdoor Erasing in Deep Neural Networks via Restricted Adversarial Distillation

Backdoor attacks have emerged as an urgent threat to Deep Neural Networks (DNNs), where victim DNNs are furtively implanted with malicious neurons that could be triggered by the adversary. To defend against backdoor attacks, many works establish a staged pipeline to remove backdoors from victim DNNs: inspecting, locating, and erasing. However, in a scenario where a few clean data can be accessible, such pipeline is fragile and cannot erase backdoors completely without sacrificing model accuracy. To address this issue, in this paper, we propose a novel data-free holistic backdoor erasing (DHBE) framework. Instead of the staged pipeline, the DHBE treats the backdoor erasing task as a unified adversarial procedure, which seeks equilibrium between two different competing processes: distillation and backdoor regularization. In distillation, the backdoored DNN is distilled into a proxy model, transferring its knowledge about clean data, yet backdoors are simultaneously transferred. In backdoor regularization, the proxy model is holistically regularized to prevent from infecting any possible backdoor transferred from distillation. These two processes jointly proceed with data-free adversarial optimization until a clean, high-accuracy proxy model is obtained. With the novel adversarial design, our framework demonstrates its superiority in three aspects: 1) minimal detriment to model accuracy, 2) high tolerance for hyperparameters, and 3) no demand for clean data. Extensive experiments on various backdoor attacks and datasets are performed to verify the effectiveness of the proposed framework. Code is available at https://github.com/yanzhicong/DHBE
How Secure Are The Main Real-World Mix Networks — Case Studies To Explore Vulnerabilities And Usability

Mix net is the most frequently used secure MPC (multi-party computation) application in the real world, where multiple routers cooperates to anonymise a batch of data. It builds an important network security mechanism to implement anonymous communication and has a wide range of applications like AI training and online services. So far, security of mix nets is only analysed in theoretic cryptographic models, and their security in real-world systems has not drawn enough attention from researchers. In this paper, several popular commercial mix net services are surveyed and they have a common strategy: developing an academic shuffling scheme into a real-world mix net system and assuming that its theoretic security properties can guarantee robustness of the systems in practical usages. Our analysis illustrates that the straightforward assumption is not reliable and a mix net has to face various challenges and attackers beyond their academic prototypes estimate. Especially, we show that in practice some users of a mix net may collude with the service providers to compromise reliability of the mix net, which is a realistic environment factor usually ignored in cryptographic protocol design. So, the anonymous communication services based on mix net in practical usage are not so reliable as widely believed and their applications in network security have non-negligible vulnerabilities or risks.
Modeling integrated service to support the work of an educational organization: An expert perspective

The functioning of the business processes of an educational organization is difficult to imagine without the use of appropriate digital tools. Today they must comply with the needs of the national educational system, taking into account external sanctions. Relevant for the education system is the creation of an integrated service that would not only eliminate the problem of “patchwork” automation, but also provide the most friendly format for interaction between the staff of an educational organization, students and customers of the educational service at all stages — from the moment of forming interest in training to maintaining communication with the graduate.That is why the aim of the work was to formalize a number of requirements due to the peculiarities of the process of providing educational services. According to the results of expert evaluation (194 experts were involved for the analysis), the author conducted clustering of evaluation results, and then identified and grouped the wishes of administrators and teachers to create the mentioned integrated service. The most significant result should be considered the revealed inconsistency of interests of the main actors of educational organization business-processes, since 80 % of experts’ assessments fell into clusters with opposite wishes. This can be used in the automation projects of educational organizations. It was noted that in the educational sphere it is important to have not only effective business processes, but also the involvement of all employees in achieving positive educational results for students.
A criterial model for assessing the quality of the digital educational environment using cloud services

Globalization of all spheres of activity of educational systems determines the need to create simple and accessible tools for monitoring and evaluating the quality of objects and resources. In particular, due to the need to intensify the processes of digital transformation of education, it is important to assess the quality of digital educational environments (DEE). The aim of the work is to substantiate the approach and describe the procedure for monitoring of DEE of educational institutions based on the available cloud services, providing convenient collection, accumulation, and processing of statistical and monitoring indicators.The projective criterial model of the system of assessment and monitoring of the quality of DEE, which is based on the results of self-assessment of the educational organization and external expert evaluation, was adopted. The main feature of the model is the participation of organizations and external experts in the process of designing criterion rubrics, conducting and analyzing the results of monitoring. Yandex Forms service is used for self-assessment of DEE and expert evaluation of the educational organization website which allows converting of the collected data into spreadsheets. By combining the data and their statistical processing it is possible to obtain indicators for different slices and ratings, to conduct a summary clustering of the DEE of the studied organizations by three levels: low, average, and high.The proposed model was implemented in the assessment of DEE practices of supplementary education for children. To determine the quantitative and qualitative indicators of the DLE and the official sites of additional education organizations we created questionnaires in Yandex Forms filled in by the representatives of the studied organizations. Pilot testing showed that the collection, processing, and delivery of monitoring data to users was technologically simple and easy.The projective criterial model for assessing the quality of the DEE of supplementary education organizations can be used for many other studies related to criterion-based assessment activities.
Framework for SQL Error Message Design: A Data-Driven Approach

Software developers use a significant amount of time reading and interpreting error messages. However, error messages have often been based on either anecdotal evidence or expert opinion, disregarding novices, who arguably are the ones who benefit the most from effective error messages. Furthermore, the usability aspects of Structured Query Language (SQL) error messages have not received much scientific attention. In this mixed-methods study, we coded a total of 128 error messages from eight database management systems (DBMS), and using data from 311 participants, analysed 4,796 queries using regression analysis to find out if and how acknowledged error message qualities explain SQL syntax error fixing success rates. Additionally, we performed a conventional content analysis on 1,505 suggestions on how to improve SQL error messages, and based on the analysis, formulated a framework consisting of nine guidelines for SQL error message design. The results indicate that general error message qualities do not necessarily explain query fixing success in the context of SQL syntax errors and that even some novel NewSQL systems fail to account for basic error message design guidelines. The error message design framework and examples of its practical applications shown in this study are applicable in educational contexts as well as by DBMS vendors in understanding novice perspectives in error message design.
Improved Whale Optimization Algorithm Based on Fusion Gravity Balance

In order to improve the shortcomings of the whale optimization algorithm (WOA) in dealing with optimization problems, and further improve the accuracy and stability of the WOA, we propose an enhanced regenerative whale optimization algorithm based on gravity balance (GWOA). In the initial stage, the nonlinear time-varying factor and inertia weight strategy are introduced to change the foraging trajectory and exploration range, which improves the search efficiency and diversity. In the random walk stage and the encircling stage, the excellent solutions are protected by the gravitational balance strategy to ensure the high quality of solution. In order to prevent the algorithm from rapidly converging to the local extreme value and failing to jump out, a regeneration mechanism is introduced to help the whale population escape from the local optimal value, and to help the whale population find a better solution within the search interval through reasonable position updating. Compared with six algorithms on 16 benchmark functions, the contribution values of each strategy and Wilcoxon rank sum test show that GWOA performs well in 30-dimensional and 100-dimensional test functions and in practical applications. In general, GWOA has better optimization ability. In each algorithm contribution experiment, compared with the WOA, the indexes of the strategies added in each stage were improved. Finally, GWOA is applied to robot path planning and three classical engineering problems, and the stability and applicability of GWOA are verified.
How Does Immigration Status and Citizenship Affect Identity Theft Victimization Risk in the US? Insights from the 2018 National Crime Victimization Survey Identity Theft Supplement

ABSTRACTABSTRACTThere has been no research study to date in the United States (US) looking at how an individual’s immigration status and citizenship might affect their odds of becoming an identity theft victim. This study fills this gap by analyzing the relationship between immigration status and identity theft victimization risk with recently released data from the 2018 iteration of the US National Crime Victimization Survey’s Identity Theft Supplement. Descriptive analysis presents demographics, risky and protective behavior, and identity theft prevalence across US-born citizens, naturalized citizens, non-citizens, and individuals with an ambiguous citizenship status in the US. Logistic regression models explore the differences between these groups in their identity theft victimization risk controlling for other demographic factors and known correlates of identity theft. Results suggest that US-born citizens, naturalized citizens, and non-citizens are at the end of the spectrum with a higher risk of identity theft victimization, whereas individuals with an ambiguous immigration status are at the lower end. Implications of the study findings and future avenues for data collection and research are discussed.KEYWORDS: Identity theftimmigrationcitizenshipundocumented immigrantsNational Crime Victimization Survey (NCVS) Disclosure statementNo potential conflict of interest was reported by the author(s).
Digital sovereignty or sovereignism? Investigating the political discourse on digital contact tracing apps in France

ABSTRACTABSTRACTIn the early months of the COVID-19 pandemic, European countries implemented several non-pharmaceutical interventions with country-specific policies and reduced coordination. Digital Contact Tracing (DCT) was one of the few interventions on which European countries had a common approach oriented toward DCT apps’ interoperability. As most EU countries implemented interoperable apps relying on an Apple and Google’s framework, France developed the app autonomously. Recent literature argues that France’s choice was mainly due to its strong stance in defense of national digital sovereignty. However, current contributions do not largely cover the issue empirically. Therefore, we aim to better explore the role played by digital sovereignty in the political debate related the development of DCT apps. To do so, we conducted a thematic analysis of 16 documents from France’s political bodies, selected from a larger corpus of 438 documents dealing with DCT. Three main relevant dimensions related to digital sovereignty emerge. First, the initially sponsored EU interoperability progressively faded in the French political debate. Then, Apple and Google’s involvement in the healthcare domain was perceived as highly problematic. Finally, having national players developing the DCT app was largely preferred.Based on our empirical findings, we further engaged with the concept of digital sovereignty, pointing out its difference from digital sovereignism by highlighting the ontological distinction between practices and ideas. Building on these reflections, we argue that France’s stance towards DCT and the related digital sovereignty practices subtended digital sovereignism positions.KEYWORDS: Digital sovereigntydigital sovereignismdigital contact tracingCOVID-19TousAntiCovidFrance Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsDario PizzulDario Pizzul is a postdoctoral researcher at the University of Pavia. He holds a PhD in ‘Analysis of Social and Economic Processes’ from the University of Milan – Bicocca. He has been working on the social construction of digital contact tracing apps during COVID-19 in Europe. In recent years, he has also been focusing on digital inequalities in Europe. He is also working on surveillance capitalism within a large project that involves several Italian Universities. email: d.pizzul@campus.unimib.itMichele VenezianoMichele Veneziano is a PhD student in Political and Social Sciences at the University of Bologna. In his doctoral project, he studies digital transformation of public administrations, investigating the role of governmental agencies and civil society organizations in the social shaping of digital public services and technologies. email: michele.veneziano2@unibo.it