Description
Linear sketches have been widely adopted to process fast data streams, and they can be used to accurately answer frequency estimation, approximate top K items, and summarize data distributions. When data are sensitive, it is desirable to provide privacy guarantees for linear sketches to preserve private information while delivering useful results with theoretical bounds. To address these challenges, we propose differentially private linear sketches with high privacy-utility trade-offs for frequency, quantile, and top K approximations.
Prochains exposés
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The Battle Against Bots: Current Threats and New Directions to Counter Automated Attacks
Orateur : Elisa Chiapponi - Amadeus IT Group
In today's digital landscape, the battle between industry and automated bots is an ever-evolving challenge. Attackers are leveraging advanced techniques such as residential proxies, CAPTCHA farms, and AI-enhanced fingerprint rotations to evade detection and execute functional abuse attacks, including web scraping, denial of inventory, and SMS pumping. This talk will explore ongoing efforts[…]-
SoSysec
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Détection d'intrusion
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Safety-Security Convergence of Industrial Control Systems
Orateur : Maxime Puys - Université Clermont Auvergne - IUT de Clermont-Ferrand
Industrial Control Systems (ICS) are designed to provide a service, such as power generation or water treatment, while protecting people, assets, and the environment against hazards. However, ICS now integrate Information Technology (IT) and are interconnected with the outside world such as the Internet, thereby exposing their infrastructures to cyberattacks. Cyberattacks have thus become new[…]-
SoSysec
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Détection d'intrusion
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