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Titlebook: Computer Security. ESORICS 2023 International Workshops; CyberICS, DPM, CBT, Sokratis Katsikas,Frédéric Cuppens,Stefanos Gritza Conference

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楼主: melancholy
发表于 2025-3-26 23:52:54 | 显示全部楼层
Synthetic Is All You Need: Removing the Auxiliary Data Assumption for Membership Inference Attacks Ae attacks (MIAs), based on shadow modeling, have become the standard to evaluate the privacy of synthetic data, they currently assume the attacker to have access to an auxiliary dataset sampled from a similar distribution as the training dataset. This is often seen as a very strong assumption in pra
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Differentially Private Traffic Flow Prediction Using Transformers: A Federated Approachisting traffic flow prediction techniques using deep learning, mostly LSTMs, have achieved enormous success based on the large traffic flow datasets collected by governments and different organizations. Nevertheless, a lot of these datasets contain sensitive attributes that may relate to users’ priv
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Analyzing Continuous K,-Anonymization for Smart Meter Data evaluated in the context of a dataset to make meaningful statements about their eligibility for a particular use case. In this paper, we therefore analyze the suitability of continuous .-anonymization with CASTLE for data streams generated by smart meters. We compare CASTLE ’s continuous, piecewise
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METRICS: A Methodology for Evaluating and Testing the Resilience of Industrial Control Systems to Cyng ICS testbeds by enabling the configuration of measurement campaigns for comprehensive resilience evaluations. Therefore, the user specifies individual evaluation scenarios consisting of cyberattacks and countermeasures while facilitating manual and automatic interventions. Moreover, . provides do
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Skade – A Challenge Management System for Cyber Threat Huntinglators are presented, e.g. the data they need to provide to enable generation of feedback to trainees. Seven required functions are identified, e.g. the ability to produce assessment questions based on logs from emulators.
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