151 citations · 373 across the 31 of their papers we have counts for
31 papers
Security Analysis for SCONE Logic Locking
Akashdeep Saha, Mohammed Nabeel, Johann Knechtel +2
SCONE [DAC'25] expands a logic locking interface with additional encoded inputs derived from the original primary inputs, and admits two realizations: a \textit{with-ES} variant, w…
CHEHAB RL: Learning to Optimize Fully Homomorphic Encryption Computations
Bilel Sefsaf, Abderraouf Dandani, Abdessamed Seddiki +4
Fully Homomorphic Encryption (FHE) enables computations directly on encrypted data, but its high computational cost remains a significant barrier. Writing efficient FHE code is a c…
@NTT: Algorithm-Targeted NTT hardware acceleration via Design-Time Constant Optimization
Mohammed Nabeel, Mahmoud Hafez, Michail Maniatakos
The Number Theoretic Transform (NTT) is a critical computational bottleneck in many lattice-based postquantum cryptographic (PQC) algorithms. By leveraging the Fast Fourier Transfo…
Fully Decentralized Certified Unlearning
Hithem Lamri, Michail Maniatakos
Machine unlearning (MU) seeks to remove the influence of specified data from a trained model in response to privacy requests or data poisoning. While certified unlearning has been…
BlendFL: Blended Federated Learning for Handling Multimodal Data Heterogeneity
Alejandro Guerra-Manzanares, Omar El-Herraoui, Michail Maniatakos +1
One of the key challenges of collaborative machine learning, without data sharing, is multimodal data heterogeneity in real-world settings. While Federated Learning (FL) enables mo…
PAPER: Privacy-Preserving Convolutional Neural Networks using Low-Degree Polynomial Approximations and Structural Optimizations on Leveled FHE
Eduardo Chielle, Manaar Alam, Jinting Liu +2
Recent work using Fully Homomorphic Encryption (FHE) has made non-interactive privacy-preserving inference of deep Convolutional Neural Networks (CNNs) possible. However, the perfo…
DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems
Hithem Lamri, Manaar Alam, Haiyan Jiang +1
Federated Unlearning (FU) enables clients to remove the influence of specific data from a collaboratively trained shared global model, addressing regulatory requirements such as GD…
Veritas: Deterministic Verilog Code Synthesis from LLM-Generated Conjunctive Normal Form
Prithwish Basu Roy, Akashdeep Saha, Manaar Alam +4
Automated Verilog code synthesis poses significant challenges and typically demands expert oversight. Traditional high-level synthesis (HLS) methods often fail to scale for real-wo…
ReVeil: Unconstrained Concealed Backdoor Attack on Deep Neural Networks using Machine Unlearning
Manaar Alam, Hithem Lamri, Michail Maniatakos
Backdoor attacks embed hidden functionalities in deep neural networks (DNN), triggering malicious behavior with specific inputs. Advanced defenses monitor anomalous DNN inferences…
RTL-Breaker: Assessing the Security of LLMs against Backdoor Attacks on HDL Code Generation
Lakshmi Likhitha Mankali, Jitendra Bhandari, Manaar Alam +4
Large language models (LLMs) have demonstrated remarkable potential with code generation/completion tasks for hardware design. In fact, LLM-based hardware description language (HDL…
LLMPot: Dynamically Configured LLM-based Honeypot for Industrial Protocol and Physical Process Emulation
Christoforos Vasilatos, Dunia J. Mahboobeh, Hithem Lamri +2
Industrial Control Systems (ICS) are extensively used in critical infrastructures ensuring efficient, reliable, and continuous operations. However, their increasing connectivity an…
Perception, performance, and detectability of conversational artificial intelligence across 32 university courses
Hazem Ibrahim, Fengyuan Liu, Rohail Asim +36
The emergence of large language models has led to the development of powerful tools such as ChatGPT that can produce text indistinguishable from human-generated work. With the incr…
HowkGPT: Investigating the Detection of ChatGPT-generated University Student Homework through Context-Aware Perplexity Analysis
Christoforos Vasilatos, Manaar Alam, Talal Rahwan +2
As the use of Large Language Models (LLMs) in text generation tasks proliferates, concerns arise over their potential to compromise academic integrity. The education sector current…
Get Rid Of Your Trail: Remotely Erasing Backdoors in Federated Learning
Manaar Alam, Hithem Lamri, Michail Maniatakos
Federated Learning (FL) enables collaborative deep learning training across multiple participants without exposing sensitive personal data. However, the distributed nature of FL an…
TREBUCHET: Fully Homomorphic Encryption Accelerator for Deep Computation
David Bruce Cousins, Yuriy Polyakov, Ahmad Al Badawi +21
Secure computation is of critical importance to not only the DoD, but across financial institutions, healthcare, and anywhere personally identifiable information (PII) is accessed.…
RPU: The Ring Processing Unit
Deepraj Soni, Negar Neda, Naifeng Zhang +14
Ring-Learning-with-Errors (RLWE) has emerged as the foundation of many important techniques for improving security and privacy, including homomorphic encryption and post-quantum cr…
Privacy-preserving machine learning for healthcare: open challenges and future perspectives
Alejandro Guerra-Manzanares, L. Julian Lechuga Lopez, Michail Maniatakos +1
Machine Learning (ML) has recently shown tremendous success in modeling various healthcare prediction tasks, ranging from disease diagnosis and prognosis to patient treatment. Due…
Optimal Smoothing Distribution Exploration for Backdoor Neutralization in Deep Learning-based Traffic Systems
Yue Wang, Wending Li, Michail Maniatakos +1
Deep Reinforcement Learning (DRL) enhances the efficiency of Autonomous Vehicles (AV), but also makes them susceptible to backdoor attacks that can result in traffic congestion or…
ICSPatch: Automated Vulnerability Localization and Non-Intrusive Hotpatching in Industrial Control Systems using Data Dependence Graphs
Prashant Hari Narayan Rajput, Constantine Doumanidis, Michail Maniatakos
The paradigm shift of enabling extensive intercommunication between the Operational Technology (OT) and Information Technology (IT) devices allows vulnerabilities typical to the IT…
PerDoor: Persistent Non-Uniform Backdoors in Federated Learning using Adversarial Perturbations
Manaar Alam, Esha Sarkar, Michail Maniatakos
Federated Learning (FL) enables numerous participants to train deep learning models collaboratively without exposing their personal, potentially sensitive data, making it a promisi…
Scalable privacy-preserving cancer type prediction with homomorphic encryption
Esha Sarkar, Eduardo Chielle, Gamze Gursoy +3
Machine Learning (ML) alleviates the challenges of high-dimensional data analysis and improves decision making in critical applications like healthcare. Effective cancer type from…
FieldFuzz: In Situ Blackbox Fuzzing of Proprietary Industrial Automation Runtimes via the Network
Andrei Bytes, Prashant Hari Narayan Rajput, Constantine Doumanidis +3
Networked Programmable Logic Controllers (PLCs) are proprietary industrial devices utilized in critical infrastructure that execute control logic applications in complex proprietar…
Accelerating Fully Homomorphic Encryption by Bridging Modular and Bit-Level Arithmetic
Eduardo Chielle, Oleg Mazonka, Homer Gamil +1
The dramatic increase of data breaches in modern computing platforms has emphasized that access control is not sufficient to protect sensitive user data. Recent advances in cryptog…
CoFHEE: A Co-processor for Fully Homomorphic Encryption Execution (Extended Version)
Mohammed Nabeel, Homer Gamil, Deepraj Soni +6
The migration of computation to the cloud has raised concerns regarding the security and privacy of sensitive data, as their need to be decrypted before processing, renders them su…