research
∙
09/21/2023
DeepTheft: Stealing DNN Model Architectures through Power Side Channel
Deep Neural Network (DNN) models are often deployed in resource-sharing ...
share
research
∙
05/22/2023
Privet: A Privacy-Preserving Vertical Federated Learning Service for Gradient Boosted Decision Tables
Vertical federated learning (VFL) has recently emerged as an appealing d...
research
∙
04/22/2023
Fast Diffusion Probabilistic Model Sampling through the lens of Backward Error Analysis
Denoising diffusion probabilistic models (DDPMs) are a class of powerful...
research
∙
02/09/2023
Imperceptible Sample-Specific Backdoor to DNN with Denoising Autoencoder
The backdoor attack poses a new security threat to deep neural networks....
research
∙
02/03/2023
Vertical Federated Learning: Taxonomies, Threats, and Prospects
Federated learning (FL) is the most popular distributed machine learning...
research
∙
11/24/2022
Tracking Dataset IP Use in Deep Neural Networks
Training highly performant deep neural networks (DNNs) typically require...
research
∙
09/06/2022
MACAB: Model-Agnostic Clean-Annotation Backdoor to Object Detection with Natural Trigger in Real-World
Object detection is the foundation of various critical computer-vision t...
research
∙
07/20/2022
MLMSA: Multi-Label Multi-Side-Channel-Information enabled Deep Learning Attacks on APUF Variants
To improve the modeling resilience of silicon strong physical unclonable...
research
∙
07/19/2022
MUD-PQFed: Towards Malicious User Detection in Privacy-Preserving Quantized Federated Learning
Federated Learning (FL), a distributed machine learning paradigm, has be...
research
∙
06/10/2022
Binarizing Split Learning for Data Privacy Enhancement and Computation Reduction
Split learning (SL) enables data privacy preservation by allowing client...
research
∙
05/31/2022
CASSOCK: Viable Backdoor Attacks against DNN in The Wall of Source-Specific Backdoor Defences
Backdoor attacks have been a critical threat to deep neural network (DNN...
research
∙
04/14/2022
On Scheduling Mechanisms Beyond the Worst Case
The problem of scheduling unrelated machines has been studied since the ...
research
∙
04/04/2022
Towards Privacy-Preserving and Verifiable Federated Matrix Factorization
Recent years have witnessed the rapid growth of federated learning (FL),...
research
∙
03/29/2022
Systematically Evaluation of Challenge Obfuscated APUFs
As a well-known physical unclonable function that can provide huge numbe...
research
∙
02/10/2022
PPA: Preference Profiling Attack Against Federated Learning
Federated learning (FL) trains a global model across a number of decentr...
research
∙
02/01/2022
Deep Reference Priors: What is the best way to pretrain a model?
What is the best way to exploit extra data – be it unlabeled data from t...
research
∙
01/21/2022
Dangerous Cloaking: Natural Trigger based Backdoor Attacks on Object Detectors in the Physical World
Deep learning models have been shown to be vulnerable to recent backdoor...
research
∙
01/09/2022
A Retrospective and Futurespective of Rowhammer Attacks and Defenses on DRAM
Rowhammer has drawn much attention from both academia and industry in th...
research
∙
11/22/2021
NTD: Non-Transferability Enabled Backdoor Detection
A backdoor deep learning (DL) model behaves normally upon clean inputs b...
research
∙
10/26/2021
SEDML: Securely and Efficiently Harnessing Distributed Knowledge in Machine Learning
Training high-performing deep learning models require a rich amount of d...
research
∙
10/03/2021
Design and Evaluate Recomposited OR-AND-XOR-PUF
Physical Unclonable Function (PUF) is a hardware security primitive with...
research
∙
09/07/2021
NoisFre: Noise-Tolerant Memory Fingerprints from Commodity Devices for Security Functions
Given the ubiquity of memory in commodity electronic devices, fingerprin...
research
∙
08/20/2021
Quantization Backdoors to Deep Learning Models
There is currently a burgeoning demand for deploying deep learning (DL) ...
research
∙
05/09/2021
RBNN: Memory-Efficient Reconfigurable Deep Binary Neural Network with IP Protection for Internet of Things
Though deep neural network models exhibit outstanding performance for va...
research
∙
04/08/2021
Can Differential Privacy Practically Protect Collaborative Deep Learning Inference for the Internet of Things?
Collaborative inference has recently emerged as an intriguing framework ...
research
∙
03/19/2021
Wisecr: Secure Simultaneous Code Disseminationto Many Batteryless Computational RFID Devices
Emerging ultra-low-power tiny scale computing devices in Cyber-Physical ...
research
∙
03/03/2021
Evaluation and Optimization of Distributed Machine Learning Techniques for Internet of Things
Federated learning (FL) and split learning (SL) are state-of-the-art dis...
research
∙
03/01/2021
Token-Modification Adversarial Attacks for Natural Language Processing: A Survey
There are now many adversarial attacks for natural language processing s...
research
∙
02/20/2021
SoftTRR: Protect Page Tables Against RowHammer Attacks using Software-only Target Row Refresh
Rowhammer attacks that corrupt level-1 page tables to gain kernel privil...
research
∙
11/01/2020
An Information-Geometric Distance on the Space of Tasks
This paper computes a distance between tasks modeled as joint distributi...
research
∙
10/08/2020
Decamouflage: A Framework to Detect Image-Scaling Attacks on Convolutional Neural Networks
As an essential processing step in computer vision applications, image r...
research
∙
07/27/2020
VFL: A Verifiable Federated Learning with Privacy-Preserving for Big Data in Industrial IoT
Due to the strong analytical ability of big data, deep learning has been...
research
∙
07/27/2020
FedEmail: Performance Measurement of Privacy-friendly Phishing Detection Enabled by Federated Learning
Artificial intelligence (AI) has been applied in phishing email detectio...
research
∙
07/21/2020
Backdoor Attacks and Countermeasures on Deep Learning: A Comprehensive Review
This work provides the community with a timely comprehensive review of b...
research
∙
03/30/2020
End-to-End Evaluation of Federated Learning and Split Learning for Internet of Things
This work is the first attempt to evaluate and compare felderated learni...
research
∙
03/16/2020
Can We Use Split Learning on 1D CNN Models for Privacy Preserving Training?
A new collaborative learning, called split learning, was recently introd...
research
∙
02/27/2020
A Free-Energy Principle for Representation Learning
This paper employs a formal connection of machine learning with thermody...
research
∙
11/23/2019
Design and Evaluation of a Multi-Domain Trojan Detection Method on Deep Neural Networks
This work corroborates a run-time Trojan detection method exploiting STR...
research
∙
06/01/2019
Average-case Analysis of the Assignment Problem with Independent Preferences
The fundamental assignment problem is in search of welfare maximization ...
research
∙
02/18/2019
STRIP: A Defence Against Trojan Attacks on Deep Neural Networks
Recent trojan attacks on deep neural network (DNN) models are one insidi...
research
∙
02/08/2019
Hash Functions and Benchmarks for Resource Constrained Passive Devices: A Preliminary Study
Recently, we have witnessed the emergence of intermittently powered comp...
research
∙
02/08/2019
Building Secure SRAM PUF Key Generators on Resource Constrained Devices
A securely maintained key is the premise upon which data stored and tran...
research
∙
07/29/2018
TREVERSE: Trial-and-Error Lightweight Secure Reverse Authentication with Simulatable PUFs
A physical unclonable function (PUF) generates hardware intrinsic volati...
research
∙
07/27/2018
SecuCode: Intrinsic PUF Entangled Secure Wireless Code Dissemination for Computational RFID Devices
The simplicity of deployment and perpetual operation of energy harvestin...
research
∙
05/19/2018
