Results 11 to 20 of about 151,312 (300)
Differential Privacy at Risk: Bridging Randomness and Privacy Budget [PDF]
Proceedings on Privacy Enhancing Technologies, 2020 AbstractThe calibration of noise for a privacy-preserving mechanism depends on the sensitivity of the query and the prescribed privacy level. A data steward must make the non-trivial choice of a privacy level that balances the requirements of users and the monetary constraints of the business entity.Firstly, we analyse roles of the sources of ...
Dandekar, Ashish +2 more
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Privacy Risks of Cybersquatting Attacks
Journal of Cybersecurity and PrivacyCybersquatting is a collection of methods commonly used by malicious actors to mislead or trick internet users into accessing fraudulent or malicious content.
Jack Kolenbrander +2 more
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Proactive Data Categorization for Privacy in DevPrivOps
InformationAssessing privacy within data-driven software is challenging due to its subjective nature and the diverse array of privacy-enhancing technologies. A simplistic personal/non-personal data classification fails to capture the nuances of data specifications ...
Catarina Silva +2 more
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Smartphone Privacy and Cyber Safety among Australian Adolescents: Gender Differences
InformationWhile existing studies explore smartphone privacy setting risks for adolescents, they provide limited insight into the role of gender in these dynamics.
Yeslam Al-Saggaf, Julie Maclean
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A Framework for the Design of Privacy-Preserving Record Linkage Systems
Journal of Cybersecurity and PrivacyRecord linkage can enhance the utility of data by bringing data together from different sources, increasing the available information about data subjects and providing more holistic views. Doing so, however, can increase privacy risks.
Zixin Nie +5 more
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The metaverse: Privacy and information security risks
International Journal of Information Management Data InsightsThe advent of the metaverse—a convergence of physical and virtual realities catalyzed by a spectrum of emerging technologies—heralds a new epoch in the digital era.
Héctor Laiz-Ibanez +2 more
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On the Privacy Risks of Model Explanations [PDF]
Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, 2021 Privacy and transparency are two key foundations of trustworthy machine learning. Model explanations offer insights into a model's decisions on input data, whereas privacy is primarily concerned with protecting information about the training data. We analyze connections between model explanations and the leakage of sensitive information about the model'
Reza Shokri +2 more
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On the Privacy Risks of Algorithmic Fairness [PDF]
2021 IEEE European Symposium on Security and Privacy (EuroS&P), 2021 Algorithmic fairness and privacy are essential pillars of trustworthy machine learning. Fair machine learning aims at minimizing discrimination against protected groups by, for example, imposing a constraint on models to equalize their behavior across different groups. This can subsequently change the influence of training data points on the fair model,
Hongyan Chang, Reza Shokri
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Understanding Risks of Privacy Theater with Differential Privacy
Proceedings of the ACM on Human-Computer Interaction, 2022 Differential privacy is one of the most popular technologies in the growing area of privacy-conscious data analytics. But differential privacy, along with other privacy-enhancing technologies, may enable privacy theater. In implementations of differential privacy, certain algorithm parameters control the tradeoff between privacy protection for ...
Mary Anne Smart +2 more
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Quantifying Location Privacy Risks Under Heterogeneous Correlations
IEEE Access, 2021 Currently, increasingly ubiquitous location-based services are facilitating the activities of people in daily life. However, releasing real locations could lead to serious concerns about privacy.
Bing Li, Hong Zhu, Meiyi Xie
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