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  1. AU="Grossklags, Jens"
  2. AU="Andreas M. Köster"
  3. AU="Diana Karolina Maniak"
  4. AU="Cahuana-Hurtado, Lucero"
  5. AU="Ebert, Christoph"
  6. AU="Köhler, Matthias"
  7. AU=Fitzgerald Amelia Lucy AU=Fitzgerald Amelia Lucy
  8. AU="Yang, Charles"
  9. AU="Fraser, Alice j"
  10. AU=MacKenzie James A
  11. AU=Guettari Moez AU=Guettari Moez
  12. AU=McLeod Carolyn
  13. AU="Patel P.M"
  14. AU="Patel N.M"
  15. AU="Naganawa, Mika"
  16. AU="Viecelli, Claudio"
  17. AU=Valls Joan
  18. AU="Yang, Qizhang"
  19. AU=Wilt Timothy J
  20. AU="Dene R. Littler" AU="Dene R. Littler"
  21. AU="Petrenko, Andrei"
  22. AU=Valentino Kristin
  23. AU=Swash M
  24. AU="Adedipe, Ifeoluwa"
  25. AU=Shen Hongcheng
  26. AU="Padhy, Biswajit"
  27. AU="Kruglikov, Alibek"
  28. AU="Tasu, Jean Pierre"
  29. AU="Floate, Kevin D"
  30. AU="Mark Rijpkema"
  31. AU="Gjeloshi, Klodian"
  32. AU="Lucie Beaudoin"

Suchergebnis

Treffer 1 - 10 von insgesamt 27

Suchoptionen

  1. Buch ; Online: Chapter 22 Social Credit System and Privacy

    Chen, Mo / Engelmann, Severin / Grossklags, Jens

    2023  

    Schlagwörter Social networking ; Communication studies ; Media studies ; AI, ICT, algorithm, artificial intelligence, communication studies, data, digital communication, digital media, information law, media and society, network, online, policy, protection, psychology, regulation, rights, security, technology
    Sprache Englisch
    Umfang 1 electronic resource (11 pages)
    Verlag Taylor and Francis
    Dokumenttyp Buch ; Online
    Anmerkung English
    HBZ-ID HT030613468
    ISBN 9781032155555 ; 1032155558
    Datenquelle ZB MED Katalog Medizin, Gesundheit, Ernährung, Umwelt, Agrar

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  2. Artikel ; Online: Comprehensive Analysis of Privacy Leakage in Vertical Federated Learning During Prediction

    Jiang Xue / Zhou Xuebing / Grossklags Jens

    Proceedings on Privacy Enhancing Technologies, Vol 2022, Iss 2, Pp 263-

    2022  Band 281

    Abstract: Vertical federated learning (VFL), a variant of federated learning, has recently attracted increasing attention. An active party having the true labels jointly trains a model with other parties (referred to as passive parties) in order to use more ... ...

    Abstract Vertical federated learning (VFL), a variant of federated learning, has recently attracted increasing attention. An active party having the true labels jointly trains a model with other parties (referred to as passive parties) in order to use more features to achieve higher model accuracy. During the prediction phase, all the parties collaboratively compute the predicted confidence scores of each target record and the results will be finally returned to the active party. However, a recent study by Luo et al. [28] pointed out that the active party can use these confidence scores to reconstruct passive-party features and cause severe privacy leakage.
    Schlagwörter vertical federated learning ; privacy attacks ; Ethics ; BJ1-1725 ; Electronic computers. Computer science ; QA75.5-76.95
    Sprache Englisch
    Erscheinungsdatum 2022-04-01T00:00:00Z
    Verlag Sciendo
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  3. Artikel ; Online: Privacy-Preserving High-dimensional Data Collection with Federated Generative Autoencoder

    Jiang Xue / Zhou Xuebing / Grossklags Jens

    Proceedings on Privacy Enhancing Technologies, Vol 2022, Iss 1, Pp 481-

    2022  Band 500

    Abstract: Business intelligence and AI services often involve the collection of copious amounts of multidimensional personal data. Since these data usually contain sensitive information of individuals, the direct collection can lead to privacy violations. Local ... ...

    Abstract Business intelligence and AI services often involve the collection of copious amounts of multidimensional personal data. Since these data usually contain sensitive information of individuals, the direct collection can lead to privacy violations. Local differential privacy (LDP) is currently considered a state-ofthe-art solution for privacy-preserving data collection. However, existing LDP algorithms are not applicable to high-dimensional data; not only because of the increase in computation and communication cost, but also poor data utility.
    Schlagwörter high-dimensional data collection ; local differential privacy ; federated learning ; generative models ; Ethics ; BJ1-1725 ; Electronic computers. Computer science ; QA75.5-76.95
    Sprache Englisch
    Erscheinungsdatum 2022-01-01T00:00:00Z
    Verlag Sciendo
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  4. Artikel ; Online: An Analysis of the Current State of the Consumer Credit Reporting System in China

    Chen Mo / Grossklags Jens

    Proceedings on Privacy Enhancing Technologies, Vol 2020, Iss 4, Pp 89-

    2020  Band 110

    Abstract: The Chinese Social Credit System (SCS), known as the first national digitally-implemented credit rating system, consists of two parallel arms: a government-run and a commercial one. The government-run arm of the SCS, especially efforts to blacklist and ... ...

    Abstract The Chinese Social Credit System (SCS), known as the first national digitally-implemented credit rating system, consists of two parallel arms: a government-run and a commercial one. The government-run arm of the SCS, especially efforts to blacklist and redlist individuals and organizations, has attracted significant attention worldwide. In contrast, the commercial part has been less often in the public spotlight except for discussions about Zhima Credit.
    Schlagwörter social credit system ; consumer credit reporting ; privacy policy ; china ; Ethics ; BJ1-1725 ; Electronic computers. Computer science ; QA75.5-76.95
    Sprache Englisch
    Erscheinungsdatum 2020-10-01T00:00:00Z
    Verlag Sciendo
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  5. Buch ; Online ; Dissertation / Habilitation: Understanding the Legitimacy of Digital Socio-Technical Classification Systems

    Engelmann, Severin Karl David Verfasser] / [Grossklags, Jens [Akademischer Betreuer] / Ziewitz, Malte Gutachter] / [Grossklags, Jens [Gutachter] / Berendt, Bettina [Gutachter]

    2023  

    Verfasserangabe Severin Karl David Engelmann ; Gutachter: Malte Ziewitz, Jens Großklags, Bettina Berendt ; Betreuer: Jens Großklags
    Schlagwörter Sozialwissenschaften, Soziologie ; Social Sciences, Sociology
    Thema/Rubrik (Code) sg300
    Sprache Englisch
    Verlag Universitätsbibliothek der TU München
    Erscheinungsort München
    Dokumenttyp Buch ; Online ; Dissertation / Habilitation
    Datenquelle Digitale Dissertationen im Internet

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  6. Buch ; Online: Data Forensics in Diffusion Models

    Zhu, Derui / Chen, Dingfan / Grossklags, Jens / Fritz, Mario

    A Systematic Analysis of Membership Privacy

    2023  

    Abstract: In recent years, diffusion models have achieved tremendous success in the field of image generation, becoming the stateof-the-art technology for AI-based image processing applications. Despite the numerous benefits brought by recent advances in diffusion ...

    Abstract In recent years, diffusion models have achieved tremendous success in the field of image generation, becoming the stateof-the-art technology for AI-based image processing applications. Despite the numerous benefits brought by recent advances in diffusion models, there are also concerns about their potential misuse, specifically in terms of privacy breaches and intellectual property infringement. In particular, some of their unique characteristics open up new attack surfaces when considering the real-world deployment of such models. With a thorough investigation of the attack vectors, we develop a systematic analysis of membership inference attacks on diffusion models and propose novel attack methods tailored to each attack scenario specifically relevant to diffusion models. Our approach exploits easily obtainable quantities and is highly effective, achieving near-perfect attack performance (>0.9 AUCROC) in realistic scenarios. Our extensive experiments demonstrate the effectiveness of our method, highlighting the importance of considering privacy and intellectual property risks when using diffusion models in image generation tasks.
    Schlagwörter Computer Science - Machine Learning ; Computer Science - Cryptography and Security
    Thema/Rubrik (Code) 303
    Erscheinungsdatum 2023-02-15
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  7. Buch ; Online: The Effectiveness of Security Interventions on GitHub

    Fischer, Felix / Höbenreich, Jonas / Grossklags, Jens

    2023  

    Abstract: In 2017, GitHub was the first online open source platform to show security alerts to its users. It has since introduced further security interventions to help developers improve the security of their open source software. In this study, we investigate ... ...

    Abstract In 2017, GitHub was the first online open source platform to show security alerts to its users. It has since introduced further security interventions to help developers improve the security of their open source software. In this study, we investigate and compare the effects of these interventions. This offers a valuable empirical perspective on security interventions in the context of software development, enriching the predominantly qualitative and survey-based literature landscape with substantial data-driven insights. We conduct a time series analysis on security-altering commits covering the entire history of a large-scale sample of over 50,000 GitHub repositories to infer the causal effects of the security alert, security update, and code scanning interventions. Our analysis shows that while all of GitHub's security interventions have a significant positive effect on security, they differ greatly in their effect size. By comparing the design of each intervention, we identify the building blocks that worked well and those that did not. We also provide recommendations on how practitioners can improve the design of their interventions to enhance their effectiveness.
    Schlagwörter Computer Science - Cryptography and Security
    Thema/Rubrik (Code) 005
    Erscheinungsdatum 2023-09-09
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  8. Buch ; Konferenzbeitrag: Decision and game theory for security

    Grossklags, Jens

    third international conference, GameSec 2012, Budapest, Hungary, November 5 - 6, 2012 ; proceedings

    (Lecture notes in computer science ; 7638)

    2012  

    Körperschaft GameSec
    Veranstaltung/Kongress GameSec (3, 2012.11.05-06, Budapest) ; International Conference on Desicion and Game Theory for Security (3, 2012.11.05-06, Budapest)
    Verfasserangabe Jens Grossklags ... (eds.)
    Serientitel Lecture notes in computer science ; 7638
    Schlagwörter Computer networks/Security measures ; Computersicherheit ; Netzwerktopologie ; Spieltheorie ; Datensicherung ; Netzwerkverwaltung ; Kryptoanalyse
    Sprache Englisch
    Umfang XII, 308 S., Ill., graph. Darst., 235 mm x 155 mm
    Verlag Springer
    Erscheinungsort Heidelberg u.a.
    Dokumenttyp Buch ; Konferenzbeitrag
    Anmerkung Literaturangaben
    ISBN 3642342655 ; 9783642342653 ; 9783642342660 ; 3642342663
    Datenquelle Katalog der Technische Informationsbibliothek Hannover

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  9. Buch ; Online: The Benefits of Vulnerability Discovery and Bug Bounty Programs

    Atefi, Soodeh / Sivagnanam, Amutheezan / Ayman, Afiya / Grossklags, Jens / Laszka, Aron

    Case Studies of Chromium and Firefox

    2023  

    Abstract: Recently, bug-bounty programs have gained popularity and become a significant part of the security culture of many organizations. Bug-bounty programs enable organizations to enhance their security posture by harnessing the diverse expertise of crowds of ... ...

    Abstract Recently, bug-bounty programs have gained popularity and become a significant part of the security culture of many organizations. Bug-bounty programs enable organizations to enhance their security posture by harnessing the diverse expertise of crowds of external security experts (i.e., bug hunters). Nonetheless, quantifying the benefits of bug-bounty programs remains elusive, which presents a significant challenge for managing them. Previous studies focused on measuring their benefits in terms of the number of vulnerabilities reported or based on the properties of the reported vulnerabilities, such as severity or exploitability. However, beyond these inherent properties, the value of a report also depends on the probability that the vulnerability would be discovered by a threat actor before an internal expert could discover and patch it. In this paper, we present a data-driven study of the Chromium and Firefox vulnerability-reward programs. First, we estimate the difficulty of discovering a vulnerability using the probability of rediscovery as a novel metric. Our findings show that vulnerability discovery and patching provide clear benefits by making it difficult for threat actors to find vulnerabilities; however, we also identify opportunities for improvement, such as incentivizing bug hunters to focus more on development releases. Second, we compare the types of vulnerabilities that are discovered internally vs. externally and those that are exploited by threat actors. We observe significant differences between vulnerabilities found by external bug hunters, internal security teams, and external threat actors, which indicates that bug-bounty programs provide an important benefit by complementing the expertise of internal teams, but also that external hunters should be incentivized more to focus on the types of vulnerabilities that are likely to be exploited by threat actors.
    Schlagwörter Computer Science - Cryptography and Security
    Thema/Rubrik (Code) 005
    Erscheinungsdatum 2023-01-28
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  10. Artikel ; Online: Towards a Model on the Factors Influencing Social App Users’ Valuation of Interdependent Privacy

    Pu Yu / Grossklags Jens

    Proceedings on Privacy Enhancing Technologies, Vol 2016, Iss 2, Pp 61-

    2016  Band 81

    Abstract: In the context of third-party social apps, the problem of interdependency of privacy refers to users making app adoption decisions which cause the collection and utilization of personal information of users’ friends. In contrast, users’ friends have ... ...

    Abstract In the context of third-party social apps, the problem of interdependency of privacy refers to users making app adoption decisions which cause the collection and utilization of personal information of users’ friends. In contrast, users’ friends have typically little or no direct influence over these decision-making processes.
    Schlagwörter third-party social apps ; interdependent privacy ; app data collection context ; value of privacy ; conjoint analysis ; structural equation modeling ; online survey study ; Ethics ; BJ1-1725 ; Electronic computers. Computer science ; QA75.5-76.95
    Sprache Englisch
    Erscheinungsdatum 2016-04-01T00:00:00Z
    Verlag Sciendo
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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