ALPINE deploys an offline-trained TD3 policy on terminal devices to map multi-dimensional risk states to adaptive privacy budgets for local differential privacy in mobile edge crowdsensing, with edge feedback closing the loop.
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Authors introduce MLM and CLM specialization methods that avoid memorizing identifiers in sensitive training data while aiming for a privacy-utility tradeoff on medical datasets.
Hierarchical self-stabilizing algorithms can evaluate intellectual property risks and identify solutions that cover all IP dimensions or suboptimal ones to reduce overall risk.
A literature review and industry survey on SDV security and privacy produces a framework for addressing mixed-criticality systems, layered defenses, privacy techniques, and harmonized vehicle-cloud protections.
citing papers explorer
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ALPINE: Closed-Loop Adaptive Privacy Budget Allocation for Mobile Edge Crowdsensing
ALPINE deploys an offline-trained TD3 policy on terminal devices to map multi-dimensional risk states to adaptive privacy budgets for local differential privacy in mobile edge crowdsensing, with edge feedback closing the loop.
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Towards the Anonymization of the Language Modeling
Authors introduce MLM and CLM specialization methods that avoid memorizing identifiers in sensitive training data while aiming for a privacy-utility tradeoff on medical datasets.
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Minimizing Intellectual Property Risks via Self-Stabilizing Algorithms
Hierarchical self-stabilizing algorithms can evaluate intellectual property risks and identify solutions that cover all IP dimensions or suboptimal ones to reduce overall risk.
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Contextualizing Security and Privacy of Software-Defined Vehicles: A Literature Review and Industry Perspectives
A literature review and industry survey on SDV security and privacy produces a framework for addressing mixed-criticality systems, layered defenses, privacy techniques, and harmonized vehicle-cloud protections.