Titlebook: Innovative Security Solutions for Information Technology and Communications; 15th International C Giampaolo Bella,Mihai Doinea,Helge Janick

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,MOTUS: How Quantized Parameters Improve Protection of Model and Its Inference Input,s paper, we discuss how a model’s parameter quantization works to protect the model and its inference inputs. To this end, we present an investigational protocol, ., based on ternary neural networks whose parameters are ternarized. Through extensive experiments with MOTUS, we found three key insight

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,Application-Oriented Anonymization Framework for Social Network Datasets and IoT Environments,ding personal and sensitive information. Anonymization enables providers to share their datasets and preserve the privacy of individuals at the same time. It is a valuable tool for preserving individuals’ privacy in social network datasets and IoT environments. Researchers recently focused on develo

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,AI-Powered Vulnerability Detection for Secure Source Code Development,d to reduce these issues at the early stages of the development lifecycle. Artificial Intelligence (AI) could be applied to detect vulnerabilities in source code. In this research, a Machine Learning (ML) based method is proposed to detect source code vulnerabilities in C/C++ applications. Furthermo

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