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09/13/2023
ZKROWNN: Zero Knowledge Right of Ownership for Neural Networks
Training contemporary AI models requires investment in procuring learnin...
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06/12/2023
AnoFel: Supporting Anonymity for Privacy-Preserving Federated Learning
Federated learning enables users to collaboratively train a machine lear...
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07/14/2022
Characterizing and Optimizing End-to-End Systems for Private Inference
Increasing privacy concerns have given rise to Private Inference (PI). I...
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06/24/2022
zPROBE: Zero Peek Robustness Checks for Federated Learning
Privacy-preserving federated learning allows multiple users to jointly t...
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11/04/2021
CryptoNite: Revealing the Pitfalls of End-to-End Private Inference at Scale
The privacy concerns of providing deep learning inference as a service h...
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06/17/2021
Sphynx: ReLU-Efficient Network Design for Private Inference
The emergence of deep learning has been accompanied by privacy concerns ...
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06/15/2021
Circa: Stochastic ReLUs for Private Deep Learning
The simultaneous rise of machine learning as a service and concerns over...
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03/12/2021
Generating and Characterizing Scenarios for Safety Testing of Autonomous Vehicles
Extracting interesting scenarios from real-world data as well as generat...
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03/02/2021
DeepReDuce: ReLU Reduction for Fast Private Inference
The recent rise of privacy concerns has led researchers to devise method...
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06/15/2020
CryptoNAS: Private Inference on a ReLU Budget
Machine learning as a service has given raise to privacy concerns surrou...
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12/04/2018
Outsourcing Private Machine Learning via Lightweight Secure Arithmetic Computation
In several settings of practical interest, two parties seek to collabora...
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02/11/2018
ThUnderVolt: Enabling Aggressive Voltage Underscaling and Timing Error Resilience for Energy Efficient Deep Neural Network Accelerators
Hardware accelerators are being increasingly deployed to boost the perfo...
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11/04/2017
