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Titlebook: Applied Cryptography and Network Security; 22nd International C Christina Pöpper,Lejla Batina Conference proceedings 2024 The Editor(s) (if

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期刊全称Applied Cryptography and Network Security
期刊简称22nd International C
影响因子2023Christina Pöpper,Lejla Batina
视频video
学科分类Lecture Notes in Computer Science
图书封面Titlebook: Applied Cryptography and Network Security; 22nd International C Christina Pöpper,Lejla Batina Conference proceedings 2024 The Editor(s) (if
影响因子The 3-volume set LNCS 14583-14585 constitutes the proceedings of the 22nd International Conference on Applied Cryptography and Network Security, ACNS 2024, which took place in  Abu Dhabi, UAE, in March 2024. .The 54 full papers included in these proceedings were carefully reviewed and selected from 230 submissions. They have been organized in topical sections as follows: .Part I: Cryptographic protocols; encrypted data; signatures; .Part II: Post-quantum; lattices; wireless and networks; privacy and homomorphic encryption; symmetric crypto; .Part III: Blockchain; smart infrastructures, systems and software; attacks; users and usability..
Pindex Conference proceedings 2024
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Programmable Payment Channelshe contract. In this paper, we make the following contributions:.We note that our implementations of . and . depend on the CREATE2 opcode which allows one to compute the deployment address of a contract (without having to deploy it).
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“Hello? Is There Anybody in There?” Leakage Assessment of Differential Privacy Mechanisms in Smart M data. In this paper, we propose a novel statistical methodology, constructed using the principles of . based hypothesis testing, to .. Additionally, we formally establish that this trade-off is an inherent characteristic of the smart metering problem, implying that any mechanism adhering to this tr
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ProvIoT : Detecting Stealthy Attacks in IoT through Federated Edge-Cloud Security detection in IoT devices. ProvIoT applies federated learning techniques to overcome data and privacy limitations while minimizing network overhead. Infrequent on-device training of the local model requires less than 10% CPU overhead; syncing with the global models requires sending and receiving .2M
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Time Is Money, Friend! Timing Side-Channel Attack Against Garbled Circuit Constructions attack against commonly employed garbled circuit frameworks. Goblin is a machine learning-assisted, non-profiling, single-trace timing side-channel attack (SCA), which successfully recovers the garbler’s input during the computation under different scenarios, including various garbling frameworks,
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