Search Results for author:
Found 7 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.
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.
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
