Optimal checkpointing for secure intermittently-powered IoT devices. - Zahra Ghodsi; Siddharth Garg; Ramesh Karri (ICCAD, 2017)

SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloud. - Zahra Ghodsi; Tianyu Gu; Siddharth Garg (NIPS, 2017)

Thundervolt: enabling aggressive voltage underscaling and timing error resilience for energy efficient deep learning accelerators. - Jeff Zhang 001; Kartheek Rangineni; Zahra Ghodsi; Siddharth Garg (DAC, 2018)

ThUnderVolt: Enabling Aggressive Voltage Underscaling and Timing Error Resilience for Energy Efficient Deep Neural Network Accelerators. - Jeff Zhang 001; Kartheek Rangineni; Zahra Ghodsi; Siddharth Garg (CoRR, 2018)

Outsourcing Private Machine Learning via Lightweight Secure Arithmetic Computation. - Siddharth Garg; Zahra Ghodsi; Carmit Hazay; Yuval Ishai; Antonio Marcedone; Muthuramakrishnan Venkitasubramaniam (CoRR, 2018)

CryptoNAS: Private Inference on a ReLU Budget. - Zahra Ghodsi; Akshaj Kumar Veldanda; Brandon Reagen; Siddharth Garg (NeurIPS, 2020)

SafeTPU: A Verifiably Secure Hardware Accelerator for Deep Neural Networks. - Maria I. Mera Collantes; Zahra Ghodsi; Siddharth Garg (VTS, 2020)

Enabling Timing Error Resilience for Low-Power Systolic-Array Based Deep Learning Accelerators. - Jeff Zhang 001; Zahra Ghodsi; Siddharth Garg; Kartheek Rangineni (IEEE Des. Test, 2020)

DeepReDuce: ReLU Reduction for Fast Private Inference. - Nandan Kumar Jha; Zahra Ghodsi; Siddharth Garg; Brandon Reagen (ICML, 2021)

Generating and Characterizing Scenarios for Safety Testing of Autonomous Vehicles. - Zahra Ghodsi; Siva Kumar Sastry Hari; Iuri Frosio; Timothy Tsai 002; Alejandro J. Troccoli; Stephen W. Keckler; Siddharth Garg; Anima Anandkumar (IV, 2021)

Circa: Stochastic ReLUs for Private Deep Learning. - Zahra Ghodsi; Nandan Kumar Jha; Brandon Reagen; Siddharth Garg (NeurIPS, 2021)

Sphynx: ReLU-Efficient Network Design for Private Inference. - Minsu Cho; Zahra Ghodsi; Brandon Reagen; Siddharth Garg; Chinmay Hegde (CoRR, 2021)

CryptoNite: Revealing the Pitfalls of End-to-End Private Inference at Scale. - Karthik Garimella; Nandan Kumar Jha; Zahra Ghodsi; Siddharth Garg; Brandon Reagen (CoRR, 2021)

Sphynx: A Deep Neural Network Design for Private Inference. - Minsu Cho; Zahra Ghodsi; Brandon Reagen; Siddharth Garg; Chinmay Hegde (IEEE Secur. Priv., 2022)

Characterizing and Optimizing End-to-End Systems for Private Inference. - Karthik Garimella; Zahra Ghodsi; Nandan Kumar Jha; Siddharth Garg; Brandon Reagen (ASPLOS (3), 2023)

ZKROWNN: Zero Knowledge Right of Ownership for Neural Networks. - Nojan Sheybani; Zahra Ghodsi; Ritvik Kapila; Farinaz Koushanfar (DAC, 2023)

AdaGL: Adaptive Learning for Agile Distributed Training of Gigantic GNNs. - Ruisi Zhang; Mojan Javaheripi; Zahra Ghodsi; Amit Bleiweiss; Farinaz Koushanfar (DAC, 2023)

zPROBE: Zero Peek Robustness Checks for Federated Learning. - Zahra Ghodsi; Mojan Javaheripi; Nojan Sheybani; Xinqiao Zhang; Ke Huang 001; Farinaz Koushanfar (ICCV, 2023)

Cuot: Accelerating Oblivious Transfer on Gpus for Privacy-Preserving Computation. - Andrew Gan; Setsuna Yuki; Timothy Rogers; Zahra Ghodsi (HOST, 2025)

AnoFel: Supporting Anonymity for Privacy-Preserving Federated Learning. - Ghada Almashaqbeh; Zahra Ghodsi (Proc. Priv. Enhancing Technol., 2025)