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Sphynx: A Deep Neural Network Design for Private Inference., , , , and . IEEE Secur. Priv., 20 (5): 22-34 (2022)AnoFel: Supporting Anonymity for Privacy-Preserving Federated Learning., and . CoRR, (2023)Generating and Characterizing Scenarios for Safety Testing of Autonomous Vehicles., , , , , , , and . IV, page 157-164. IEEE, (2021)ZKROWNN: Zero Knowledge Right of Ownership for Neural Networks., , , and . DAC, page 1-6. IEEE, (2023)Optimal checkpointing for secure intermittently-powered IoT devices., , and . ICCAD, page 376-383. IEEE, (2017)zPROBE: Zero Peek Robustness Checks for Federated Learning., , , , , and . ICCV, page 4837-4847. IEEE, (2023)AdaGL: Adaptive Learning for Agile Distributed Training of Gigantic GNNs., , , , and . DAC, page 1-6. IEEE, (2023)ThUnderVolt: Enabling Aggressive Voltage Underscaling and Timing Error Resilience for Energy Efficient Deep Neural Network Accelerators., , , and . CoRR, (2018)SafeTPU: A Verifiably Secure Hardware Accelerator for Deep Neural Networks., , and . VTS, page 1-6. IEEE, (2020)Circa: Stochastic ReLUs for Private Deep Learning., , , and . NeurIPS, page 2241-2252. (2021)