Search Results for author:
Found 9 papers, 3 papers with code
Harvesting Private Medical Images in Federated Learning Systems with Crafted Models
However, in this paper, we propose a novel attack named MediLeak that enables a malicious parameter server to recover high-fidelity patient images from the model updates uploaded by the clients.
BoBa: Boosting Backdoor Detection through Data Distribution Inference in Federated Learning
Based on the intuition that clustering and subsequent backdoor detection can drastically benefit from knowing client data distributions, we propose a novel data distribution inference mechanism.
NoiSec: Harnessing Noise for Security against Adversarial and Backdoor Attacks
However, the proliferation of malicious data manipulation attacks against ML, namely adversarial and backdoor attacks, jeopardizes its reliability in safety-critical applications.
ProFLingo: A Fingerprinting-based Intellectual Property Protection Scheme for Large Language Models
Our scheme assesses the effectiveness of these queries on a suspect model to determine whether it has been derived from the original model.
Learning-augmented Online Minimization of Age of Information and Transmission Costs
To address this challenge, we develop a robust online algorithm to minimize the sum of transmission and staleness costs, ensuring a worst-case performance guarantee.
Scale-MIA: A Scalable Model Inversion Attack against Secure Federated Learning via Latent Space Reconstruction
The first step is to reconstruct the latent space representations (LSRs) from the aggregated model updates using a closed-form inversion mechanism, leveraging specially crafted linear layers.
CANShield: Deep Learning-Based Intrusion Detection Framework for Controller Area Networks at the Signal-Level
As ordinary injection attacks disrupt the typical timing properties of the CAN data stream, rule-based intrusion detection systems (IDS) can easily detect them.
AoI-minimizing Scheduling in UAV-relayed IoT Networks
Our simulation results show that DQN-based scheduler outperforms MAF-MAD scheduler and three other baseline schedulers, i. e., Maximal AoI First (MAF), Round Robin (RR) and Random, employed at both hops under general conditions when the network is small (with 10's of IoT devices).
Enjoy the Untrusted Cloud: A Secure, Scalable and Efficient SQL-like Query Framework for Outsourcing Data
On one hand, such work lacks of supporting scalable access control over multiple data users.
Cryptography and Security Databases Distributed, Parallel, and Cluster Computing
