Titlebook: Computer Security – ESORICS 2024; 29th European Sympos Joaquin Garcia-Alfaro,Rafał Kozik,Sokratis Katsika Conference proceedings 2024 The E

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Exploiting Layerwise Feature Representation Similarity For Backdoor Defence in Federated Learningf FedAvgCKA across various federated learning scenarios and datasets, showcasing its ability to maintain high main task accuracy and significantly reduce backdoor attack success rates even in non-iid settings.

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Soumia Benkrid,Ladjel Bellatrecheng the extraction of keyword matches from leaked data. Empirical evaluations conducted on multiple real-world datasets demonstrate a notable enhancement in query recovery accuracy, up to 19.5%. We also analyze the performance of the proposed attack in the presence of diverse countermeasures.

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Similar Data is Powerful: Enhancing Inference Attacks on SSE with Volume Leakagesng the extraction of keyword matches from leaked data. Empirical evaluations conducted on multiple real-world datasets demonstrate a notable enhancement in query recovery accuracy, up to 19.5%. We also analyze the performance of the proposed attack in the presence of diverse countermeasures.

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Conference proceedings 2024from 535 submissions. They were organized in topical sections as follows:..Part I: Security and Machine Learning...Part II: Network, Web, Hardware and Cloud; Privacy and Personal Datat Protection...Part III: Software and Systems Security; Applied Cryptopgraphy...Part IV: Attacks and Defenses; Miscellaneous..

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Comparative Analysis of OpenACC Compilers relies on untampered trusted time slices with valid lengths and cannot be bypassed via arbitrary timer preemption. Consequently, the privileged adversary is constrained to performing preemption attacks within trusted time slices solely via non-timer interrupts, which are confidentially monitored wi