188 citations · 221 across the 12 of their papers we have counts for
14 papers
The Innocent Courier: Covert Exfiltration Through Legitimate LLM Web Fetching
Alessandro Pegoraro, Daryan Merx, Phillip Rieger +1
With the increasing capabilities of Large-Language-Models (LLMs) and LLM-based agents, users are increasingly using them to solve everyday problems, such as answering e-mails or pr…
LightSplit: Practical Privacy-Preserving Split Learning via Orthogonal Projections (Full Version)
Mert Cihangiroglu, Alessandro Pegoraro, Phillip Rieger +2
Split learning (SL) enables collaborative training by partitioning a neural network across clients and a central server. However, the cut-layer interface introduces a key challenge…
ZORRO: Zero-Knowledge Robustness and Privacy for Split Learning (Full Version)
Nojan Sheybani, Alessandro Pegoraro, Jonathan Knauer +4
Split Learning (SL) is a distributed learning approach that enables resource-constrained clients to collaboratively train deep neural networks (DNNs) by offloading most layers to a…
SafeSplit: A Novel Defense Against Client-Side Backdoor Attacks in Split Learning (Full Version)
Phillip Rieger, Alessandro Pegoraro, Kavita Kumari +3
Split Learning (SL) is a distributed deep learning approach enabling multiple clients and a server to collaboratively train and infer on a shared deep neural network (DNN) without…
Phantom: Untargeted Poisoning Attacks on Semi-Supervised Learning (Full Version)
Jonathan Knauer, Phillip Rieger, Hossein Fereidooni +1
Deep Neural Networks (DNNs) can handle increasingly complex tasks, albeit they require rapidly expanding training datasets. Collecting data from platforms with user-generated conte…
FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning
Hossein Fereidooni, Alessandro Pegoraro, Phillip Rieger +2
Federated learning (FL) is a collaborative learning paradigm allowing multiple clients to jointly train a model without sharing their training data. However, FL is susceptible to p…
FLEDGE: Ledger-based Federated Learning Resilient to Inference and Backdoor Attacks
Jorge Castillo, Phillip Rieger, Hossein Fereidooni +2
Federated learning (FL) is a distributed learning process that uses a trusted aggregation server to allow multiple parties (or clients) to collaboratively train a machine learning…
FLAIRS: FPGA-Accelerated Inference-Resistant & Secure Federated Learning
Huimin Li, Phillip Rieger, Shaza Zeitouni +2
Federated Learning (FL) has become very popular since it enables clients to train a joint model collaboratively without sharing their private data. However, FL has been shown to be…
ARGUS: Context-Based Detection of Stealthy IoT Infiltration Attacks
Phillip Rieger, Marco Chilese, Reham Mohamed +3
IoT application domains, device diversity and connectivity are rapidly growing. IoT devices control various functions in smart homes and buildings, smart cities, and smart factorie…
AuthentiSense: A Scalable Behavioral Biometrics Authentication Scheme using Few-Shot Learning for Mobile Platforms
Hossein Fereidooni, Jan König, Phillip Rieger +5
Mobile applications are widely used for online services sharing a large amount of personal data online. One-time authentication techniques such as passwords and physiological biome…
BayBFed: Bayesian Backdoor Defense for Federated Learning
Kavita Kumari, Phillip Rieger, Hossein Fereidooni +2
Federated learning (FL) allows participants to jointly train a machine learning model without sharing their private data with others. However, FL is vulnerable to poisoning attacks…
CrowdGuard: Federated Backdoor Detection in Federated Learning
Phillip Rieger, Torsten Krauß, Markus Miettinen +2
Federated Learning (FL) is a promising approach enabling multiple clients to train Deep Neural Networks (DNNs) collaboratively without sharing their local training data. However, F…
DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspection
Phillip Rieger, Thien Duc Nguyen, Markus Miettinen +1
Federated Learning (FL) allows multiple clients to collaboratively train a Neural Network (NN) model on their private data without revealing the data. Recently, several targeted po…
FLAME: Taming Backdoors in Federated Learning (Extended Version 1)
Thien Duc Nguyen, Phillip Rieger, Huili Chen +10
Federated Learning (FL) is a collaborative machine learning approach allowing participants to jointly train a model without having to share their private, potentially sensitive loc…