7 citations · 18 across the 14 of their papers we have counts for
14 papers
PAC to the Future: Zero-Knowledge Proofs of PAC Private Systems
Guilhem Repetto, Nojan Sheybani, Gabrielle De Micheli +1
Privacy concerns in machine learning systems have grown significantly with the increasing reliance on sensitive user data for training large-scale models. This paper introduces a n…
Optimizing Privacy-Preserving Primitives to Support LLM-Scale Applications
Yaman Jandali, Ruisi Zhang, Nojan Sheybani +1
Privacy-preserving technologies have introduced a paradigm shift that allows for realizable secure computing in real-world systems. The significant barrier to the practical adoptio…
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…
Zero-Knowledge Proof Frameworks: A Systematic Survey
Nojan Sheybani, Anees Ahmed, Michel Kinsy +1
Zero-Knowledge Proofs (ZKPs) are a cryptographic primitive that allows a prover to demonstrate knowledge of a secret value to a verifier without revealing anything about the secret…
Robust and Secure Code Watermarking for Large Language Models via ML/Crypto Codesign
Ruisi Zhang, Neusha Javidnia, Nojan Sheybani +1
This paper introduces RoSeMary, the first-of-its-kind ML/Crypto codesign watermarking framework that regulates LLM-generated code to avoid intellectual property rights violations a…
Gotta Hash 'Em All! Speeding Up Hash Functions for Zero-Knowledge Proof Applications
Nojan Sheybani, Tengkai Gong, Anees Ahmed +3
Collision-resistant cryptographic hash functions (CRHs) are crucial for security, particularly for message authentication in Zero-knowledge Proof (ZKP) applications. However, tradi…
AMAZE: Accelerated MiMC Hardware Architecture for Zero-Knowledge Applications on the Edge
Anees Ahmed, Nojan Sheybani, Davi Moreno +4
Collision-resistant, cryptographic hash (CRH) functions have long been an integral part of providing security and privacy in modern systems. Certain constructions of zero-knowledge…
SPAM: Secure & Private Aircraft Management
Yaman Jandali, Nojan Sheybani, Farinaz Koushanfar
With the rising use of aircrafts for operations ranging from disaster-relief to warfare, there is a growing risk of adversarial attacks. Malicious entities often only require the l…
LiveTune: Dynamic Parameter Tuning for Feedback-Driven Optimization
Soheil Zibakhsh Shabgahi, Nojan Sheybani, Aiden Tabrizi +1
Feedback-driven optimization, such as traditional machine learning training, is a static process that lacks real-time adaptability of hyperparameters. Tuning solutions for optimiza…
ZKROWNN: Zero Knowledge Right of Ownership for Neural Networks
Nojan Sheybani, Zahra Ghodsi, Ritvik Kapila +1
Training contemporary AI models requires investment in procuring learning data and computing resources, making the models intellectual property of the owners. Popular model waterma…
NetFlick: Adversarial Flickering Attacks on Deep Learning Based Video Compression
Jung-Woo Chang, Nojan Sheybani, Shehzeen Samarah Hussain +3
Video compression plays a significant role in IoT devices for the efficient transport of visual data while satisfying all underlying bandwidth constraints. Deep learning-based vide…
Tailor: Altering Skip Connections for Resource-Efficient Inference
Olivia Weng, Gabriel Marcano, Vladimir Loncar +7
Deep neural networks use skip connections to improve training convergence. However, these skip connections are costly in hardware, requiring extra buffers and increasing on- and of…
FastStamp: Accelerating Neural Steganography and Digital Watermarking of Images on FPGAs
Shehzeen Hussain, Nojan Sheybani, Paarth Neekhara +3
Steganography and digital watermarking are the tasks of hiding recoverable data in image pixels. Deep neural network (DNN) based image steganography and watermarking techniques are…
zPROBE: Zero Peek Robustness Checks for Federated Learning
Zahra Ghodsi, Mojan Javaheripi, Nojan Sheybani +3
Privacy-preserving federated learning allows multiple users to jointly train a model with coordination of a central server. The server only learns the final aggregation result, thu…