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Artykuły w czasopismach na temat „Empirical privacy defenses”

Autor: Grafiati

Data publikacji: 11 stycznia 2025

Data aktualizacji: 31 lipca 2025

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1

Kaplan, Caelin, Chuan Xu, Othmane Marfoq, Giovanni Neglia, and Anderson Santana de Oliveira. "A Cautionary Tale: On the Role of Reference Data in Empirical Privacy Defenses." Proceedings on Privacy Enhancing Technologies 2024, no. 1 (2024): 525–48. http://dx.doi.org/10.56553/popets-2024-0031.

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Within the realm of privacy-preserving machine learning, empirical privacy defenses have been proposed as a solution to achieve satisfactory levels of training data privacy without a significant drop in model utility. Most existing defenses against membership inference attacks assume access to reference data, defined as an additional dataset coming from the same (or a similar) underlying distribution as training data. Despite the common use of reference data, previous works are notably reticent about defining and evaluating reference data privacy. As gains in model utility and/or training data

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Nakai, Tsunato, Ye Wang, Kota Yoshida, and Takeshi Fujino. "SEDMA: Self-Distillation with Model Aggregation for Membership Privacy." Proceedings on Privacy Enhancing Technologies 2024, no. 1 (2024): 494–508. http://dx.doi.org/10.56553/popets-2024-0029.

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Membership inference attacks (MIAs) are important measures to evaluate potential risks of privacy leakage from machine learning (ML) models. State-of-the-art MIA defenses have achieved favorable privacy-utility trade-offs using knowledge distillation on split training datasets. However, such defenses increase computational costs as a large number of the ML models must be trained on the split datasets. In this study, we proposed a new MIA defense, called SEDMA, based on self-distillation using model aggregation to mitigate the MIAs, inspired by the model parameter averaging as used in federated

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Ozdayi, Mustafa Safa, Murat Kantarcioglu, and Yulia R. Gel. "Defending against Backdoors in Federated Learning with Robust Learning Rate." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 10 (2021): 9268–76. http://dx.doi.org/10.1609/aaai.v35i10.17118.

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Federated learning (FL) allows a set of agents to collaboratively train a model without sharing their potentially sensitive data. This makes FL suitable for privacy-preserving applications. At the same time, FL is susceptible to adversarial attacks due to decentralized and unvetted data. One important line of attacks against FL is the backdoor attacks. In a backdoor attack, an adversary tries to embed a backdoor functionality to the model during training that can later be activated to cause a desired misclassification. To prevent backdoor attacks, we propose a lightweight defense that requires

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Primus, Eve. "The Problematic Structure of Indigent Defense Delivery." Michigan Law Review, no. 122.2 (2023): 205. http://dx.doi.org/10.36644/mlr.122.2.problematic.

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The national conversation about criminal justice reform largely ignores the critical need for structural reforms in the provision of indigent defense. In most parts of the country, decisions about how to structure the provision of indigent defense are made at the local level, resulting in a fragmented patchwork of different indigent defense delivery systems. In most counties, if an indigent criminal defendant gets representation at all, it comes from assigned counsel or flat-fee contract lawyers rather than public defenders. In those assigned-counsel and flat-fee contract systems, the lawyers

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Wang, Tianhao, Yuheng Zhang, and Ruoxi Jia. "Improving Robustness to Model Inversion Attacks via Mutual Information Regularization." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 13 (2021): 11666–73. http://dx.doi.org/10.1609/aaai.v35i13.17387.

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This paper studies defense mechanisms against model inversion (MI) attacks -- a type of privacy attacks aimed at inferring information about the training data distribution given the access to a target machine learning model. Existing defense mechanisms rely on model-specific heuristics or noise injection. While being able to mitigate attacks, existing methods significantly hinder model performance. There remains a question of how to design a defense mechanism that is applicable to a variety of models and achieves better utility-privacy tradeoff. In this paper, we propose the Mutual Information

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Sangero, Boaz. "A New Defense for Self-Defense." Buffalo Criminal Law Review 9, no. 2 (2006): 475–559. http://dx.doi.org/10.1525/nclr.2006.9.2.475.

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Abstract Private defense, like self-defense, has been virtually undisputed both in the past and present and even taken for granted, and perhaps particularly for this reason, sufficient attention has not always been given to the rationale underlying private defense. As a result, the legal arrangements set for private defense in the different legal systems are deficient, inconsistent, and, at times, replete with internal contradictions. This article seeks to propose a sound rationale for the concept of private defense. It begins by attempting to clearly and precisely delineate the scope of the d

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Chen, Jiyu, Yiwen Guo, Qianjun Zheng, and Hao Chen. "Protect privacy of deep classification networks by exploiting their generative power." Machine Learning 110, no. 4 (2021): 651–74. http://dx.doi.org/10.1007/s10994-021-05951-6.

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AbstractResearch showed that deep learning models are vulnerable to membership inference attacks, which aim to determine if an example is in the training set of the model. We propose a new framework to defend against this sort of attack. Our key insight is that if we retrain the original classifier with a new dataset that is independent of the original training set while their elements are sampled from the same distribution, the retrained classifier will leak no information that cannot be inferred from the distribution about the original training set. Our framework consists of three phases. Fi

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8

Miao, Lu, Weibo Li, Jia Zhao, Xin Zhou, and Yao Wu. "Differential Private Defense Against Backdoor Attacks in Federated Learning." Frontiers in Computing and Intelligent Systems 9, no. 2 (2024): 31–39. http://dx.doi.org/10.54097/dyt1nn60.

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Federated learning has been applied in a wide variety of applications, in which clients upload their local updates instead of providing their datasets to jointly train a global model. However, the training process of federated learning is vulnerable to adversarial attacks (e.g., backdoor attack) in presence of malicious clients. Previous works showed that differential privacy (DP) can be used to defend against backdoor attacks, at the cost of vastly losing model utility. In this work, we study two kinds of backdoor attacks and propose a method based on differential privacy, called Clip Norm De

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9

Abbasi Tadi, Ali, Saroj Dayal, Dima Alhadidi, and Noman Mohammed. "Comparative Analysis of Membership Inference Attacks in Federated and Centralized Learning." Information 14, no. 11 (2023): 620. http://dx.doi.org/10.3390/info14110620.

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The vulnerability of machine learning models to membership inference attacks, which aim to determine whether a specific record belongs to the training dataset, is explored in this paper. Federated learning allows multiple parties to independently train a model without sharing or centralizing their data, offering privacy advantages. However, when private datasets are used in federated learning and model access is granted, the risk of membership inference attacks emerges, potentially compromising sensitive data. To address this, effective defenses in a federated learning environment must be deve

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PERSKY, JOSEPH. "Rawls's Thin (Millean) Defense of Private Property." Utilitas 22, no. 2 (2010): 134–47. http://dx.doi.org/10.1017/s0953820810000051.

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This article suggests that Rawls's break with early utilitarians is not so much over the greatest happiness principle as it is over the relation of the institution of private property to justice. In this respect Rawls is very close to John Stuart Mill, arguing for a cleansed or tamed version of the institution. That said, Rawls's defense of private property remains very thin and highly idealized, again following Mill. If Hume and Bentham fail to demonstrate their claims, Rawls and Mill do little better. Rawls, like Mill, has constructed a challenging standard, admits to severe limitations on o

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11

Cherubin, Giovanni. "Bayes, not Naïve: Security Bounds on Website Fingerprinting Defenses." Proceedings on Privacy Enhancing Technologies 2017, no. 4 (2017): 215–31. http://dx.doi.org/10.1515/popets-2017-0046.

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Abstract Website Fingerprinting (WF) attacks raise major concerns about users’ privacy. They employ Machine Learning (ML) techniques to allow a local passive adversary to uncover the Web browsing behavior of a user, even if she browses through an encrypted tunnel (e.g. Tor, VPN). Numerous defenses have been proposed in the past; however, it is typically difficult to have formal guarantees on their security, which is most often evaluated empirically against state-of-the-art attacks. In this paper, we present a practical method to derive security bounds for any WF defense, where the bounds depen

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12

Sannajust, Aurelie, Mohamed Arouri, and Frederic Teulon. "Motivations Of Public To Private Transactions: An International Empirical Investigation." Journal of Applied Business Research (JABR) 31, no. 1 (2014): 1. http://dx.doi.org/10.19030/jabr.v31i1.8979.

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This article contributes to the financial literature by investigating the motivations of Public to Private transactions in an international perspective (Europe, USA and Asia). We consider seven main possible motivations: tax savings, incentive realignment, control, free cash-flow, growth of prospects, takeover defense and undervaluation. Our empirical findings suggest that low growth prospects, low liquidity and high free cash-flow are the three main motivations for a Public to Private transactions. However, regions show some particularities such as importance of family block-holders in Europe

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13

Wainakh, Aidmar, Fabrizio Ventola, Till Müßig, et al. "User-Level Label Leakage from Gradients in Federated Learning." Proceedings on Privacy Enhancing Technologies 2022, no. 2 (2022): 227–44. http://dx.doi.org/10.2478/popets-2022-0043.

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Abstract Federated learning enables multiple users to build a joint model by sharing their model updates (gradients), while their raw data remains local on their devices. In contrast to the common belief that this provides privacy benefits, we here add to the very recent results on privacy risks when sharing gradients. Specifically, we investigate Label Leakage from Gradients (LLG), a novel attack to extract the labels of the users’ training data from their shared gradients. The attack exploits the direction and magnitude of gradients to determine the presence or absence of any label. LLG is s

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14

Wu, Jing, Munawar Hayat, Mingyi Zhou, and Mehrtash Harandi. "Concealing Sensitive Samples against Gradient Leakage in Federated Learning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 19 (2024): 21717–25. http://dx.doi.org/10.1609/aaai.v38i19.30171.

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Federated Learning (FL) is a distributed learning paradigm that enhances users' privacy by eliminating the need for clients to share raw, private data with the server. Despite the success, recent studies expose the vulnerability of FL to model inversion attacks, where adversaries reconstruct users’ private data via eavesdropping on the shared gradient information. We hypothesize that a key factor in the success of such attacks is the low entanglement among gradients per data within the batch during stochastic optimization. This creates a vulnerability that an adversary can exploit to reconstru

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15

Pan, Bin, Shih-Yung Wei, Xuanhua Xu, and Wei-Chiang Hong. "The Impact of Defense Investment on Economic Growth in the Perspective of Time Series." International Journal of Applied Evolutionary Computation 5, no. 4 (2014): 44–58. http://dx.doi.org/10.4018/ijaec.2014100104.

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By considering the demand and supply effects of defense investment and the uncertainty of the stochastic process of the production and defense investment, this study proposes a stochastic endogenous growth model to explore the impact of defense investment on economic growth. The results suggest that the relationship between defense investment and economic growth rate is nonlinear and obtains the optimal percentage of defense investment to maximize economic growth. Moreover, the impact of defense investment volatility on economic growth rate is subject to production and defense investment inter

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Zhong, Da, Ruotong Yu, Kun Wu, Xiuling Wang, Jun Xu, and Wendy Hui Wang. "Disparate Vulnerability in Link Inference Attacks against Graph Neural Networks." Proceedings on Privacy Enhancing Technologies 2023, no. 4 (2023): 149–69. http://dx.doi.org/10.56553/popets-2023-0103.

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Graph Neural Networks (GNNs) have been widely used in various graph-based applications. Recent studies have shown that GNNs are vulnerable to link-level membership inference attacks (LMIA) which can infer whether a given link was included in the training graph of a GNN model. While most of the studies focus on the privacy vulnerability of the links in the entire graph, none have inspected the privacy risk of specific subgroups of links (e.g., links between LGBT users). In this paper, we present the first study of disparity in subgroup vulnerability (DSV) of GNNs against LMIA. First, with exten

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Lyu, Yifei, Hao Zeng, Jie Wang, and Yuhua Zhang. "Empirical Analysis on Unexpected Information Effect of Monetary Policy and Stock Price Fluctuation: Taking Military and Defense Enterprises as an Example." Discrete Dynamics in Nature and Society 2022 (September 24, 2022): 1–7. http://dx.doi.org/10.1155/2022/5680745.

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The existing studies have shown that the unexpected information effect of monetary policy (IEOMP) would affect the fluctuation of stock prices. This paper tests the effect of unexpected IEOMP on stock prices in China. Taking Chinese listed military and defense enterprises, for example, the author further explored how the influence of unexpected IEOMP over stock price varies between industries. The empirical analysis reveals that, despite the industrial heterogeneity of the said influence in China, the stock price of Chinese listed military and defense enterprises is still affected by unexpecte

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18

Loureiro MEDEIROS, Rodrigo, and Darc Antonio da Luz COSTA. "SOME CONSIDERATIONS ON DEFENSE INDUSTRIAL BASE (DIB) AND MILITARY LOGISTICS." RINTERPAP - Revista Interdisciplinar de Pesquisas Aplicadas 1, no. 1 (2025): 23–35. https://doi.org/10.47682/2675-6552.v1i1.102.

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This article examines the strategic interrelations between the Defense Industrial Base (DIB) and military logistics, emphasizing their critical role in ensuring technological sovereignty, strategic autonomy, and the operational readiness of Brazil’s Armed Forces. Drawing on a critical literature review and empirical cases, it discusses the structural challenges faced by the Brazilian DIB, including deindustrialization, technological dependence, and fiscal constraints imposed by neoliberal policies. The paper further explores the importance of innovation, regional cooperation, and public-privat

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19

Ward, Jeffrey T., and Nathan W. Link. "Financial Sanctions in Pennsylvania." Federal Sentencing Reporter 34, no. 2-3 (2022): 166–72. http://dx.doi.org/10.1525/fsr.2022.34.2-3.166.

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While potential adverse consequences of carrying criminal justice debt are well documented, less is known empirically about the degree to which both assessed amounts reflect ability to pay and how repayment success varies for individuals with dissimilar financial means. This study uses fine, cost, and restitution imposition and collection data from the Administrative Office of the Pennsylvania Courts (AOPC) that spans a ten year period to examine whether there are differences in assessment and outstanding debt balances between defendants with private counsel and defendants with public defender

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Moffitt, Robert A. "In Defense of the NSF Economics Program." Journal of Economic Perspectives 30, no. 3 (2016): 213–34. http://dx.doi.org/10.1257/jep.30.3.213.

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The NSF Economics program funds basic research in economics across all its disparate fields. Its budget has experienced a long period of stagnation and decline, with its real value in 2013 below that in 1980 and having declined by 50 percent as a percent of the total NSF budget. The number of grants made by the program has also declined over time, and its current budget is very small compared to that of many other funders of economic research. Over the years, NSF-supported research has supported many of the major intellectual developments in the discipline that have made important contribution

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Weiss, Moritz. "From Wealth to Power? The Failure of Layered Reforms in India's Defense Sector." Journal of Global Security Studies 4, no. 4 (2019): 560–78. http://dx.doi.org/10.1093/jogss/ogz036.

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Abstract This article puts forward a historical institutionalist explanation of how rising powers translate increased wealth into military strength. It develops microfoundations for path dependence and applies them empirically as an approach to defense procurement. The Indian government layered market reforms onto a state-run defense sector. It aimed to exploit competition in its massive acquisition of combat fighter aircraft after 2007. Yet, despite formal rule changes and overwhelming material benefits, government reformers ultimately failed and returned to an intergovernmental purchase in 2

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Llesh, Lleshaj, and Lleshaj Vilson. "MILITARY EXPENDITURE EVALUATION: EMPIRICAL EVIDENCE FROM ALBANIA." Annali d'Italia 42 (April 25, 2023): 16–22. https://doi.org/10.5281/zenodo.7865193.

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Purpose of the research: Even though military investments are considered capital expenditures that do not motivate the private sector, it gives special importance to national security or defense by preventing various military aggressions, terrorism, border defending, and whole global peace. As a consequence, military spending constitutes a very peculiar category of public expenditure. Motivating by this fact, the article deals with an empirical investigation of the determinants of military expenditures of Albania. Design/methodology: The statistical approach used is a general multiple regressi

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Fondevila, Gustavo, and Miguel Quintana-Navarrete. "Pre-trial detention and legal defence in Latin America." International Journal of Law in Context 17, no. 1 (2021): 75–90. http://dx.doi.org/10.1017/s1744552320000440.

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AbstractPre-trial detention is used extensively in Latin America as a systematic practice implemented by courts. Despite this fact, few empirical studies have analysed the role of defence attorneys in pre-trial incarceration. This paper attempts to describe the actions taken by lawyers in order to free their clients during the judicial process, using new empirical evidence collected from the incarcerated population in Argentina, Brazil, El Salvador, Chile, Mexico and Peru. Results suggest that public defenders request conditional release less frequently but do so more effectively than their pr

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Espindola, Juan. "Educación privada e impuestos: ¿Es justo el régimen fiscal de las escuelas particulares en México?" education policy analysis archives 24 (November 21, 2016): 118. http://dx.doi.org/10.14507/epaa.24.2531.

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This paper describes and evaluates the tax regime that benefits private schools in Mexico, particularly in Mexico City. Its central claim is that this regime is incompatible with educational equality and justice. The main contribution of the paper is to examine primary and secondary empirical sources in light of the normative argument guiding this work. The first section of this work clarifies the central issues that will be addressed in the paper. The second section briefly describes the key features of the Mexican private educational sector and its tax regime. Finally, section three argues t

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Wang, Xiuling, and Wendy Hui Wang. "Subgraph Structure Membership Inference Attacks against Graph Neural Networks." Proceedings on Privacy Enhancing Technologies 2024, no. 4 (2024): 268–90. http://dx.doi.org/10.56553/popets-2024-0116.

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Graph Neural Networks (GNNs) have been widely applied to various applications across different domains. However, recent studies have shown that GNNs are susceptible to the membership inference attacks (MIAs) which aim to infer if some particular data samples were included in the model’s training data. While most previous MIAs have focused on inferring the membership of individual nodes and edges within the training graph, we introduce a novel form of membership inference attack called the Structure Membership Inference Attack (SMIA) which aims to determine whether a given set of nodes correspo

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Kumar, Chetan, Riazat Ryan, and Ming Shao. "Adversary for Social Good: Protecting Familial Privacy through Joint Adversarial Attacks." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 11304–11. http://dx.doi.org/10.1609/aaai.v34i07.6791.

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Social media has been widely used among billions of people with dramatical participation of new users every day. Among them, social networks maintain the basic social characters and host huge amount of personal data. While protecting user sensitive data is obvious and demanding, information leakage due to adversarial attacks is somehow unavoidable, yet hard to detect. For example, implicit social relation such as family information may be simply exposed by network structure and hosted face images through off-the-shelf graph neural networks (GNN), which will be empirically proved in this paper.

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Cohee, Garrett Lane, Jeff Barrows, and Rob Handfield. "Early supplier integration in the US defense industry." Journal of Defense Analytics and Logistics 3, no. 1 (2019): 2–28. http://dx.doi.org/10.1108/jdal-03-2018-0007.

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Purpose Each year, the US defense industry outsources nearly $400 bn of domestic goods and services through competitive bids. These procurement activities are quite often complex and specialized in nature because of a highly regulated federal acquisition contracting environment. Ongoing calls to improve supplier management and drive innovation in the defense industry offers an opportunity to adopt Early Supplier Integration (ESI) initiatives that have proven successful in the private sector. This paper identifies critical ESI activities and acquisition practices that the defense industry shoul

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Andrade-Neto, João Batista de. "Scientific studies on Krav Maga: state of the art." Sociology International Journal 7, no. 4 (2023): 177–86. http://dx.doi.org/10.15406/sij.2023.07.00341.

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This study aimed to analyze and synthesize the main concepts about Krav Maga in the last four decades. This is a review of the academic literature with a qualitative approach and a narrative nature. The searched databases are PubMed/Medline, Scopus, Web of Science, SciELO, Index Medicus, Capes Platform, ProQuest Digital Dissertations, Brazilian Digital Library of Theses and Dissertations (BDTD) and Google Scholar, between the years 1982 and 2022. Empirical studies with cross-sectional, longitudinal, case, control and randomized design were included, and books and private websites were excluded

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Perunova, O. "Ensuring gender equality in the workplace in the security and defense sector." Uzhhorod National University Herald. Series: Law 3, no. 84 (2024): 176–81. http://dx.doi.org/10.24144/2307-3322.2024.84.3.26.

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Gender inequality in the workplace remains a pressing issue in many industries, and the security and defense sector is no exception. The security and defense sector encompasses a system of state authorities, the Armed Forces of Ukraine, other military formations established in accordance with Ukrainian law, law enforcement and intelligence agencies, state bodies with law enforcement functions, civil protection forces, and the defense-industrial complex of Ukraine, which has historically been male-dominated, creating unique challenges for women seeking to serve in these bodies. Research indicat

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Shmidt, A. A. "Legal Foundations of State Policy on Self-Defense Weapons: In Quest of a Balance between Public and Private Interests." Actual Problems of Russian Law 19, no. 10 (2024): 45–57. http://dx.doi.org/10.17803/1994-1471.2024.167.10.045-057.

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The debate on the right of citizens to carry short-barreled weapons, including firearms of limited destruction, has been going on for more than 30 years. The essence of these debates goes far beyond the limits of administrative law, since it is not limited only by the administrative law mechanism for implementing the procedure for acquiring, carrying and using weapons, acting as part of the public legal mechanism for ensuring personal security, security of the society and the State. The relevance of the issues of civilian arms trafficking is aggravated by the complication of the criminal situa

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송정현. "A Study on the Growth Engines of Japan's Defense Industry: Empirical Analysis on the Spin-on-Off Effect between Military and Private Industries." Journal of North-east Asian Cultures 1, no. 61 (2019): 311–24. http://dx.doi.org/10.17949/jneac.1.61.201912.018.

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Wang, Xiuling, and Wendy Hui Wang. "GCL-Leak: Link Membership Inference Attacks against Graph Contrastive Learning." Proceedings on Privacy Enhancing Technologies 2024, no. 3 (2024): 165–85. http://dx.doi.org/10.56553/popets-2024-0073.

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Graph contrastive learning (GCL) has emerged as a successful method for self-supervised graph learning. It involves generating augmented views of a graph by augmenting its edges and aims to learn node embeddings that are invariant to graph augmentation. Despite its effectiveness, the potential privacy risks associated with GCL models have not been thoroughly explored. In this paper, we delve into the privacy vulnerability of GCL models through the lens of link membership inference attacks (LMIA). Specifically, we focus on the federated setting where the adversary has white-box access to the no

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Oluwabukunmi Folarin Ogunjinmi. "Cybersecurity in telecommunications: Defending networks against emerging threats." World Journal of Advanced Engineering Technology and Sciences 14, no. 3 (2025): 410–24. https://doi.org/10.30574/wjaets.2025.14.3.0138.

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The telecommunications sector, a cornerstone of global communication and critical infrastructure, faces escalating cybersecurity challenges as digital transformation accelerates. The proliferation of advanced technologies such as 5G, IoT, and cloud computing has introduced unprecedented vulnerabilities, making telecommunications networks prime targets for sophisticated cyber-attacks. This study examines the evolving landscape of cybersecurity in telecommunications, focusing on emerging threats and the efficacy of current defense mechanisms. Employing a mixed-methods approach, the research inte

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Hewa Majeed Zangana, Harman Salih Mohammed, and Mamo Muhamad Husain. "Banking Cybersecurity: Safeguarding Financial Information in the Digital Era." Journal of Computers and Digital Business 4, no. 2 (2025): 56–63. https://doi.org/10.56427/jcbd.v4i2.751.

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This study explores the escalating cybersecurity challenges in the banking sector and the potential of large language models (LLMs) to enhance digital defense mechanisms. Employing a qualitative methodology that includes a systematic literature review, expert interviews, and case study evaluations, the research investigates the integration of LLMs in cybersecurity operations such as threat detection, automated incident response, and user authentication. The findings reveal that LLMs offer significant advantages in real-time anomaly detection, predictive analytics, and natural language-based se

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Malik, Anum, Kaleem Arshid, Nooruddin Noonari, and Rizwan Munir. "Artificial Intelligence-Driven Cybersecurity Framework Using Machine Learning for Advanced Threat Detection and Prevention." Scholars Journal of Engineering and Technology 13, no. 06 (2025): 401–23. https://doi.org/10.36347/sjet.2025.v13i06.005.

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The escalating complexity, frequency, and diversity of cyber threats in today's hyper-connected digital landscape have rendered traditional security frameworks insufficient. In response, this research introduces a comprehensive, Al-driven cybersecurity architecture underpinned by state-of-the-art machine learning (ML) algorithms and the Artificial Neural Network-Interpretive Structural Modeling (ANN-ISM) paradigm. The proposed system is engineered to deliver real-time threat detection, advanced vulnerability assessment, intelligent risk response, and scalable threat mitigation capabilities. Th

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Licina, Derek. "Hospital Ships Adrift? Part 1: A Systematic Literature Review Characterizing US Navy Hospital Ship Humanitarian and Disaster Response, 2004-2012." Prehospital and Disaster Medicine 28, no. 3 (2013): 230–38. http://dx.doi.org/10.1017/s1049023x13000149.

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AbstractBackgroundUnited States foreign policy is tied extensively to health initiatives, many related to the use of military assets. Despite substantial resource investment by the US Department of Defense (DoD) in hospital ship humanitarian assistance and disaster response missions, the impact of this investment is unclear.MethodsA systematic literature review of both peer-reviewed and grey literature using eight databases representing the international community and multiple sectors was conducted. Data on the characteristics of missions directly related to US Navy hospital ship humanitarian

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Bodolica, Virginia. "An examination into the markets for corporate control: Evidence on merger and acquisition deals involving Qatari companies." Corporate Ownership and Control 10, no. 4 (2013): 439–53. http://dx.doi.org/10.22495/cocv10i4c5art1.

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Although a rich body of literature on acquisition deals and their distinguishing features exists in Western business settings, the current understanding of the governance discipline instituted by the market for corporate control in the emerging and rapidly growing economy of Qatar is severely underdeveloped. As the country is seeking to achieve its vision of becoming a preferred financial hub in the MENA region by hosting mega sport events and attracting significant foreign investments, the importance of the local market for corporate control as an external governance mechanism cannot be negle

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Shulga, Olga. "Confidentiality and scam in the internet." University Economic Bulletin, no. 48 (March 30, 2021): 76–91. http://dx.doi.org/10.31470/2306-546x-2021-48-76-91.

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The purpose of the work is to consider the theoretical and practical aspects of fraud in the Internet sphere and on this basis to identify ways to ensure the confidentiality and cybersecurity of private users and commercial organizations. The methodological basis of the work is the use of general and special methods of scientific knowledge. Methods of combining analysis and synthesis, induction and deduction have been used to identify different types of fraud in the Internet. Generalization methods, logical and empirical, were used in determining the directions of development of the national c

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Rafael, Maria Katarina E., and Chris Mai. "Understanding the Burden of Legal Financial Obligations on Indigent Washingtonians." Federal Sentencing Reporter 34, no. 2-3 (2022): 173–74. http://dx.doi.org/10.1525/fsr.2022.34.2-3.173.

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In criminal courts across the country, judges assess a variety of fines, fees and other legal financial obligations (LFOs) that many defendants struggle to pay. This paper provides a summary of the authors’ longer empirical article that examines the disproportionate burden that fine and fee assessment and collection practices impose on low-income, system-involved individuals, using administrative court data for criminal cases filed in Washington’s courts of limited jurisdiction between 2015 and 2020. The authors find that the majority of defendants do not or only partially pay their LFOs, but

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Forés. "Beyond Gathering the ‘Low-Hanging Fruit’ of Green Technology for Improved Environmental Performance: an Empirical Examination of the Moderating Effects of Proactive Environmental Management and Business Strategies." Sustainability 11, no. 22 (2019): 6299. http://dx.doi.org/10.3390/su11226299.

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This study draws on the natural resource-based view and the dynamic capabilities theory to analyse the effects of green technology, proactive environmental management and business strategy on environmental performance. This paper offers a novel analysis of the direct effect of the integrated use of green technology on environmental performance, considering a non-linear relationship between them, and how this impact can be leveraged through a formal proactive environmental planning, coordination and control system, and a defined business strategy. In order to test the research hypotheses, multi

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Lester, J. C. "ELEUTHERIC-CONJECTURAL LIBERTARIANISM: A CONCISE PHILOSOPHICAL EXPLANATION." MEST Journal 10, no. 2 (2022): 111–23. http://dx.doi.org/10.12709/mest.10.10.02.12.

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The two purposes of this essay. The general philosophical problem with most versions of social libertarianism and how this essay will proceed. The specific problem with liberty explained by a thought-experiment. The positive and abstract theory of interpersonal liberty-in-itself as ‘the absence of interpersonal initiated constraints on want-satisfaction’, for short ‘no initiated imposed costs’. The individualistic liberty-maximisation theory solves the problems of clashes, defences, and rectifications without entailing libertarian consequentialism. The practical implications of instantiating l

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De Pedro, Kris M. Tunac, Ron Avi Astor, Rami Benbenishty, Jose Estrada, Gabrielle R. Dejoie Smith, and Monica Christina Esqueda. "The Children of Military Service Members." Review of Educational Research 81, no. 4 (2011): 566–618. http://dx.doi.org/10.3102/0034654311423537.

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The wars in Afghanistan and Iraq have led to concerning psychological, behavioral, and academic outcomes for children in military families. Of the 1.2 million school-aged children of military service members, only 86,000 actually attend schools administered by the Department of Defense on military installations throughout the world. The remaining military children attend schools administered by civilian public schools, private schools, and other civilian-run educational agencies. At present, there is a knowledge gap in educational research regarding military-connected schools and students. Giv

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Bekhterev, Sergei L., and Lyudmila N. Bekhtereva. "Early Soviet Regional History in the Fates of Compatriots Through the Example of G.K. Ozhigov’s Biography." RUDN Journal of Russian History 22, no. 3 (2023): 456–69. http://dx.doi.org/10.22363/2312-8674-2023-22-3-456-469.

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The early Soviet period in the life of Grigory Kondratievich Ozhigov (Ozhegov) (1878-1935) is reconstructed within the author’s paper. A native of a Vyatka peasant family, a worker at the Izhevsk defense factories, a Socialist-Revolutionary militant, as well as a member of the All-Russian Central Executive Committee of the first convocation, who was at party work in the Baltic States and Finland, Orzhigov had a varied career The authors through their work have introduced into scientific use new sources analyzed in the context of the theory of social adaptation, through anthropological approach

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Tran, Mai Van, Tuwanont Phattharathanasut, Haymarn Soe Nyunt, Nalinthip Ekapong, and Lewis Young. "Pro-democracy platform advocacy: Resisting Big Tech-mediated authoritarianism in Southeast Asia." Open Research Europe 5 (January 20, 2025): 10. https://doi.org/10.12688/openreseurope.18820.1.

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Background Global platforms, such as Meta, YouTube, X (formerly Twitter), TikTok, and Telegram, have faced widespread criticisms for facilitating authoritarian repression of dissident voices, especially in the Global South. In response, human rights defenders have increasingly launched advocacy efforts toward the foreign platforms to defend free speech. Despite the varying forms and effects of such transnational efforts, there lacks research that systematically examines their dynamics. Methods This study advances a concept of pro-democracy platform advocacy and scrutinises the extent to which

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Tran, Mai Van, Tuwanont Phattharathanasut, Haymarn Soe Nyunt, Nalinthip Ekapong, and Lewis Young. "Pro-democracy platform advocacy: Resisting Big Tech-mediated authoritarianism in Southeast Asia." Open Research Europe 5 (May 8, 2025): 10. https://doi.org/10.12688/openreseurope.18820.2.

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Background Global platforms, such as Meta, YouTube, X (formerly Twitter), TikTok, and Telegram, have faced widespread criticisms for facilitating authoritarian repression of dissident voices, especially in the Global South. In response, human rights defenders have increasingly launched advocacy efforts toward the foreign platforms to defend free speech. Despite the varying forms and effects of such transnational efforts, there lacks research that systematically examines their dynamics. Methods This study advances a concept of pro-democracy platform advocacy and scrutinises the extent to which

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Schuetze, Benjamin. "Simulating, marketing, and playing war: US–Jordanian military collaboration and the politics of commercial security." Security Dialogue 48, no. 5 (2017): 431–50. http://dx.doi.org/10.1177/0967010617717620.

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The King Abdullah II Special Operations Training Center (KASOTC) was financed and established by the US Department of Defense, is operated by a US private business, and is owned by the Jordanian army. It not only offers a base for the training of international Special Forces and Jordanian border guards, but also for military adventure holidays, corporate leadership programs, and stunt training for actors. This article provides an analysis of the processes and technologies involved in US–Jordanian military collaboration by investigating some of the ways in which war is simulated, marketed, and

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Qiao, Yanqi, Dazhuang Liu, Rui Wang, and Kaitai Liang. "Stealthy Backdoor Attack Against Federated Learning Through Frequency Domain by Backdoor Neuron Constraint and Model Camouflage." IEEE Journal on Emerging and Selected Topics in Circuits and Systems 2024 14, no. 4 (2024): 661–72. https://doi.org/10.1109/JETCAS.2024.3450527.

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Federated Learning (FL) is a beneficial decentralized learning approach for preserving the privacy of local datasets of distributed agents. However, the distributed property of FL and untrustworthy data introducing the vulnerability to backdoor attacks. In this attack scenario, an adversary manipulates its local data with a specific trigger and trains a malicious local model to implant the backdoor. During inference, the global model would misbehave for any input with the trigger to the attacker-chosen prediction. Most existing backdoor attacks against FL focus on bypassing defense mechanisms,

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Grin'ko, Aleksey. "The rights of the defendant and public interest in proving insanity under the law of New York." Sravnitel noe konstitucionnoe obozrenie 30, no. 3 (2021): 108–24. http://dx.doi.org/10.21128/1812-7126-2021-3-108-124.

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Allocation of the burden of proof is a key issue of criminal procedure that is affected by multiple legal and social factors. Under due process principles, the defendant’s right to a fair and impartial trial is deemed to be the epicenter of the whole structure. However, efficient law enforcement is a prominent public interest that must be considered. This article explores the correlation between public and private interest in proving insanity under the law of New York, which provides great empirical background due to its long history of legal disputes and legislative changes. Considering the n

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Chen, Xiuli, and Joohan Ryoo. "Strategic Role of Artificial Intelligence for Sustainable Cooperation in Northeast Asia." American Journal of Business Science Philosophy (AJBSP) 1, no. 2 (2025): p170–184. https://doi.org/10.70122/ajbsp.v1i2.19.

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This research explores the strategic use of artificial intelligence (AI) to enhance sustainable cooperation in Northeast Asia among China, Japan, and South Korea towards UN SDGs. The research uses a mixed methodology approach to understand how AI can bring peace, economic resilience, and environmental sustainability to the region. This paper traces the evolution of cyber security, green technology, and financial partnerships among China, Japan, and South Korea by examining their past and current trilateral meetings. Using Porter’s Diamond Model, a comprehensive statistical analysis that descri

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Magalhães, Tiago De Oliveira. "Sobre certas dissimilaridades entre as Investigações filosóficas de Wittgenstein e o behaviorismo radical de Skinner [On certain dissimilarities between Wittgenstein's Philosophical investigations and Skinner's radical behaviorism]." Princípios: Revista de Filosofia (UFRN) 24, no. 43 (2017): 175. http://dx.doi.org/10.21680/1983-2109.2017v24n43id10297.

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A oposição ao dualismo, ao mentalismo, ao reducionismo e ao referencialismo, bem como a cuidadosa atenção dada ao comportamento, vêm sendo corretamente apontadas como similaridades entre o Behaviorismo Radical de Skinner e a filosofia tardia de Wittgenstein. Com o intuito de contribuir para uma comparação mais completa entre as obras desses autores, este artigo procura evidenciar pontos em torno dos quais eles divergem. Em linhas gerais, algumas das dissimilaridades mais significativas consistem em, contrariamente a Wittgenstein, Skinner alinhar-se à metafilosofia naturalista, dar ênfase à nat

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