10 papers
MOAT: Model-Agnostic Randomized Transformations for preventing Efficiency Degradation Attacks on ViTs
Anadi Goyal, Nandish Chattopadhyay, Chandan Karfa +2
To adopt the Vision Transformers (ViTs) in resource-constrained environment, token pruning is widely used to reduce computational cost without impacting accuracy. However, adversar…
SMARM+: Analyzing and Enhancing Shuffled Measurements for Remote Attestation in Real-Time IoT Settings
Amarin Laohajirapan, Norrathep Rattanavipanon
Remote attestation (RA) is a lightweight security primitive for detecting software compromise on IoT devices. Traditional RA schemes require atomic, non-interruptible memory measur…
Exploring CKKS Parameter Trade-offs for Privacy-Preserving Personalized Federated Learning
Kamolchanok Saengtong, Phanwadee Sinthong, Norrathep Rattanavipanon
Privacy-preserving Personalized Federated Learning (PFL) enables clients to collaboratively train personalized models without exposing raw data, but exchanged model updates remain…
Verifiable and Confidential DNN Inference on Low-End Edge Devices
Mohamed Khalil Kiri, Ivan De Oliveira Nunes, Aurélien Francillon +1
Deploying deep neural network (DNN) inference on low-end edge devices raises two key challenges: protecting model confidentiality against a potentially compromised edge system and…
FDM: A Framework for Decision-making to build ML-based Malware detection systems
Tadiwa Vhito, Jakapan Suaboot, Warodom Werapun +1
Selecting appropriate machine learning (ML) configurations for malware detection is a complex, multi-criteria problem. Model choice, feature engineering, and update mechanisms must…
Exploring Cross-Client Memorization of Training Data in Large Language Models for Federated Learning
Tinnakit Udsa, Can Udomcharoenchaikit, Patomporn Payoungkhamdee +2
Federated learning (FL) enables collaborative training without raw data sharing, but still risks training data memorization. Existing FL memorization detection techniques focus on…