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This paper presents EdFROST, a new threshold EdDSA (a cryptographic signature scheme used in distributed systems) protocol that detects malicious behavior more efficiently than previous methods while reducing computational overhead from zero-knowledge proofs (mathematical techniques that prove something is true without revealing how). The authors also propose a weighted threshold signature system that prevents powerful participants from dominating decisions and uses game theory (the study of strategic decision-making) with blockchain incentives to encourage honest behavior and resist DDoS attacks (attempts to overwhelm a system with traffic).

This research presents a method to classify encrypted internet traffic (HTTPS, a protocol that scrambles data sent over the internet) by reconstructing the original application data sizes hidden beneath encryption layers. The researchers developed an algorithm called LC-MRNN (Length-Correction Multiple Regression Neural Network, a type of machine learning model) to accurately restore these hidden data lengths, which helps network administrators and security teams identify what applications users are running, even when the actual data is encrypted.

Deep neural networks (DNNs, machine learning models with many layers that learn patterns from data) are vulnerable to adversarial attacks, where small, carefully crafted changes to input data trick the AI into making wrong predictions, especially in critical areas like self-driving cars. This paper presents AI-Shielder, a method that intentionally embeds backdoors (hidden pathways that alter how the model behaves) into neural networks to detect and block adversarial attacks while keeping the AI's normal performance intact. Testing shows AI-Shielder reduces successful attacks from 91.8% to 3.8% with only minor slowdowns.

This research presents SEOMA, a new system for searchable encryption (SE, a method that lets users store encrypted data on servers while still being able to search it by keywords without revealing the data's contents). The system improves on existing approaches by supporting multiple users accessing the same data while also verifying that the data owner is legitimate and preventing malicious owners from uploading fake encrypted files. SEOMA uses attribute encryption (a technique that controls who can decrypt data based on their characteristics) and access control policies to manage which users can access what data, while using less computing power and bandwidth than previous solutions.

Machine unlearning (the process of removing a user's data from a trained AI model) needs verification to confirm that genuine user data was actually deleted, but current methods using backdoors (hidden triggers added to test if data is gone) can't properly verify removal of real user samples. This paper proposes SMS, or Self-Supervised Model Seeding, which embeds user-specific identifiers into the model's internal representation to directly link users' actual data with the model, enabling better verification that genuine samples were truly unlearned.

Healthcare organizations are collecting more patient data than ever, which creates privacy risks. This research proposes GFKMC (Generalization First k-Member Clustering), a new privacy method that protects patient identities by grouping similar records together while keeping the data useful for analysis, and it works better than older methods by losing less information when privacy protection is increased.

This research presents a method for detecting moving objects in encrypted video without decrypting it, protecting privacy when video processing is done in the cloud. The approach uses selective encryption (encrypting only certain parts of compressed video) and extracts motion information from encrypted video data, then applies deep learning with attention mechanisms (a technique that helps the AI focus on important regions) to identify moving objects even with incomplete information.

This paper presents ASGA, a method for creating adversarial attacks (small, crafted changes meant to trick AI models) on video action recognition systems (AI models that identify what actions people are performing in videos). The key innovation is that attackers can compute perturbations (the malicious changes) just once on important keyframes (selected frames that represent the video's content), then replicate these changes across the entire video, making the attack work even when the model samples frames differently and reducing computational cost.

This research studies federated learning (FL, a method where multiple devices collaboratively train an AI model without sending their data to a central server) on real IoT and edge devices (small computing devices like phones and sensors) rather than in simulated environments. The study examines how FL performs in realistic conditions, focusing on heterogeneous scenarios (situations where devices have different computing power, network speeds, and data types), and provides insights to help researchers and practitioners build more practical FL systems.