Thematische Bibliographien / Empirical privacy defenses

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Auswahl der wissenschaftlichen Literatur zum Thema „Empirical privacy defenses“

Autor: Grafiati

Veröffentlicht am 11. Januar 2025

Zuletzt aktualisiert am 31. Juli 2025

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Zeitschriftenartikel zum Thema "Empirical privacy defenses"

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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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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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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Dissertationen zum Thema "Empirical privacy defenses"

Kaplan, Caelin. "Compromis inhérents à l'apprentissage automatique préservant la confidentialité." Electronic Thesis or Diss., Université Côte d'Azur, 2024. http://www.theses.fr/2024COAZ4045.

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À mesure que les modèles d'apprentissage automatique (ML) sont de plus en plus intégrés dans un large éventail d'applications, il devient plus important que jamais de garantir la confidentialité des données des individus. Cependant, les techniques actuelles entraînent souvent une perte d'utilité et peuvent affecter des facteurs comme l'équité et l'interprétabilité. Cette thèse vise à approfondir la compréhension des compromis dans trois techniques de ML respectueuses de la vie privée : la confidentialité différentielle, les défenses empiriques, et l'apprentissage fédéré, et à proposer des méth

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Spiekermann, Sarah, Jana Korunovska, and Christine Bauer. "Psychology of Ownership and Asset Defense: Why People Value their Personal Information Beyond Privacy." 2012. http://epub.wu.ac.at/3630/1/2012_ICIS_Facebook.pdf.

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Analysts, investors and entrepreneurs have for long recognized the value of comprehensive user profiles. While there is a market for trading such personal information among companies, the users, who are actually the providers of such information, are not asked to the negotiations table. To date, there is little information on how users value their personal information. In an online survey-based experiment 1059 Facebook users revealed how much they would be willing to pay for keeping their personal information. Our study reveals that as soon as people learn that some third party is interested i

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Bücher zum Thema "Empirical privacy defenses"

Lafollette, Hugh. The Empirical Evidence. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190873363.003.0006.

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I summarize the proffered evidence of the benefits and the costs of private gun ownership. I focus on the common argument that privately owning firearms is a vital means of self-defense. I isolate the two pillars of this argument: one, that there are 2.5 million defensive gun uses (DGUs) each year; two, that requiring states to issue gun carry permits to any adult who is not expressly disqualified (former felons or mentally ill) saves countless lives. I then summarize the empirical arguments offered by pro-control advocates: high gun prevalence increases homicides, suicides, and gun accidents.

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Lafollette, Hugh. In Defense of Gun Control. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190873363.001.0001.

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The gun control debate is more complex than most disputants acknowledge. We are not tasked with answering a single question: Should we have gun control? There are three distinct policy questions confronting us: Who should we permit to have which guns, and how should we regulate the acquisition, storage, and carrying of guns people may legitimately own? To answer these questions we must decide whether (and which) people have a right to bear arms, what kind of right they have, and how stringent it is. We must also evaluate divergent empirical claims about (a) the role of guns in causing harm, an

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Ganz, Aurora. Fuelling Insecurity. Policy Press, 2021. http://dx.doi.org/10.1332/policypress/9781529216691.001.0001.

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This book explores energy securitization in Azerbaijan through a sociological approach that combines discourse with a practice-oriented analysis. The study focuses on the national, international and private actors involved in the labour of energy security and their diverse sets of practices. Its empirical findings indicate that in Azerbaijan, energy securitization lacks the unitary and homogeneous character of its ideal type. Its heterogeneity interlaces internal security with external security, military with civil, defence with enforcement, coercion with control. It relies on surveillance and

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Heinze, Eric. Toward a Legal Concept of Hatred. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190465544.003.0006.

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Antidiscrimination law focuses on material conduct. A legal concept of hatred, by contrast, focuses on attitudes, as manifest notably through hate speech bans. Democracies by definition assign higher-law status to expression within public discourse. Such expression can, in principle, be legally curtailed only through a showing that it would likely cause some legally cognizable harm. Defenders of bans, struggling with standard empirical claims, have overtly or tacitly applied “anti-Cartesian” phenomenological and sociolinguistic theories to challenge dominant norms that largely limit such harm

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Clifton, Judith, Daniel Díaz Fuentes, and David Howarth, eds. Regional Development Banks in the World Economy. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198861089.001.0001.

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Regional development banks (RDB) have become increasingly important in the world economy, but have also been relatively under-researched to date. This timely volume addresses this lack of attention by providing a comprehensive, comparative, and empirically informed analysis of their origins, evolution, and contemporary role in the world economy through to the second decade of the twenty-first century. The editors provide an analytical framework that includes a revised categorization of RDB by geographic operation and function. In part one, the chapter authors offer detailed analyses of the ori

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Buchteile zum Thema "Empirical privacy defenses"

Augsberg, Ino. "In Defence of Ambiguity." In Methodology in Private Law Theory. Oxford University PressOxford, 2024. http://dx.doi.org/10.1093/oso/9780198885306.003.0006.

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Abstract The aim of classical legal methodology is to obtain unambiguous answers to clearly defined legal questions. However, a closer look shows that this goal is not only missed de facto, but also de iure. The law itself contains concepts that undermine its own disambiguation. This perspective could also provide a different view of the contrast between so-called ‘realist’, i.e. empirical understandings of law and more formalistic or dogmatic approaches. Traditionally, formalism is supposed to enable the coherence of the law towards the outside world, thus ensuring internal consistency. Howev

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Xu, Qiongka, Trevor Cohn, and Olga Ohrimenko. "Fingerprint Attack: Client De-Anonymization in Federated Learning." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2023. http://dx.doi.org/10.3233/faia230590.

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Federated Learning allows collaborative training without data sharing in settings where participants do not trust the central server and one another. Privacy can be further improved by ensuring that communication between the participants and the server is anonymized through a shuffle; decoupling the participant identity from their data. This paper seeks to examine whether such a defense is adequate to guarantee anonymity, by proposing a novel fingerprinting attack over gradients sent by the participants to the server. We show that clustering of gradients can easily break the anonymization in a

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Fabre, Cécile. "Economic Espionage." In Spying Through a Glass Darkly. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780198833765.003.0005.

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Economic espionage is a tried and tested tool of statecraft. Rulers have long resorted to it so as to help their own firms gain a competitive commercial advantage; strengthen national security; promote their citizens’ vital interests; and advance their geopolitical and strategic aims on the world stage. There is little scholarly work in that area. The stupefyingly extensive empirical literature on espionage tends to concentrate on state-on-state intelligence activities. This chapter provides a qualified defence of state-sponsored economic espionage against private businesses. It starts with a

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Marneffe, Peter de. "Self-Sovereignty, Drugs, and Prostitution." In Oxford Studies in Political Philosophy Volume 9. Oxford University PressOxford, 2023. http://dx.doi.org/10.1093/oso/9780198877639.003.0009.

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Abstract Portugal and the state of Oregon have decriminalized drugs, but they have not legalized them. There are no criminal penalties for using drugs or possessing small quantities, but there are criminal penalties for the commercial manufacture and sale of drugs. Sweden, Norway, and Denmark have decriminalized prostitution, but they have not legalized it. There are no criminal penalties for the sale of sexual services by private individuals, but there are criminal penalties for operating a sex business such as a brothel or escort agency. This chapter defends one possible rationale for these

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Bagg, Samuel Ely. "What Is State Capture?" In The Dispersion of Power. Oxford University PressOxford, 2024. http://dx.doi.org/10.1093/oso/9780192848826.003.0005.

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Abstract This chapter begins to articulate the core ideal defended in the book: democracy as resisting state capture. This ideal conceives democracy as a set of practices that help to promote the public interest by protecting public power from capture at the hands of any group. The aim of this chapter is to elaborate the core concept of “state capture,” and it begins by examining its relationship to other key terms such as democracy and the public interest, before exploring the very diverse range of forms state capture can take. Defined as the use of public power to pursue private interests at

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Konferenzberichte zum Thema "Empirical privacy defenses"

Costa, Miguel, and Sandro Pinto. "David and Goliath: An Empirical Evaluation of Attacks and Defenses for QNNs at the Deep Edge." In 2024 IEEE 9th European Symposium on Security and Privacy (EuroS&P). IEEE, 2024. http://dx.doi.org/10.1109/eurosp60621.2024.00035.

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Jankovic, Aleksandar, and Rudolf Mayer. "An Empirical Evaluation of Adversarial Examples Defences, Combinations and Robustness Scores." In CODASPY '22: Twelveth ACM Conference on Data and Application Security and Privacy. ACM, 2022. http://dx.doi.org/10.1145/3510548.3519370.

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Ferreira, Raul, Vagner Praia, Heraldo Filho, Fabrício Bonecini, Andre Vieira, and Felix Lopez. "Platform of the Brazilian CSOs: Open Government Data and Crowdsourcing for the Promotion of Citizenship." In XIII Simpósio Brasileiro de Sistemas de Informação. Sociedade Brasileira de Computação, 2017. http://dx.doi.org/10.5753/sbsi.2017.6021.

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In Brazil and around the world, Civil Society Organizations (CSOs) provide valuable public services for society. Through CSOs, people have organized and defended their rights, communities and interests, and can fully exercise their collective potential, often acting in partnership with governments to carry out public policies and/or develop their own projects, financed by the private financing or being self-sucient. Public transparency and availability of quality data are requirements for analyzing the strength and capacity of these organizations. Understanding the distribution of non-governme

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