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07/05/2023
SoK: Privacy-Preserving Data Synthesis
As the prevalence of data analysis grows, safeguarding data privacy has ...
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09/08/2022
Uncovering the Connection Between Differential Privacy and Certified Robustness of Federated Learning against Poisoning Attacks
Federated learning (FL) provides an efficient paradigm to jointly train ...
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09/08/2022
Privacy of Autonomous Vehicles: Risks, Protection Methods, and Future Directions
Recent advances in machine learning have enabled its wide application in...
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07/25/2022
SecretGen: Privacy Recovery on Pre-Trained Models via Distribution Discrimination
Transfer learning through the use of pre-trained models has become a gro...
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08/14/2021
LinkTeller: Recovering Private Edges from Graph Neural Networks via Influence Analysis
Graph structured data have enabled several successful applications such ...
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03/20/2021
DataLens: Scalable Privacy Preserving Training via Gradient Compression and Aggregation
Recent success of deep neural networks (DNNs) hinges on the availability...
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06/21/2019
Scalable Differentially Private Generative Student Model via PATE
Recent rapid development of machine learning is largely due to algorithm...
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01/03/2019
Secure Two-Party Feature Selection
In this work, we study how to securely evaluate the value of trading dat...
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11/26/2018
Distributed and Secure ML with Self-tallying Multi-party Aggregation
Privacy preserving multi-party computation has many applications in area...
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02/13/2018
Understanding Membership Inferences on Well-Generalized Learning Models
Membership Inference Attack (MIA) determines the presence of a record in...
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01/24/2018
CommanderSong: A Systematic Approach for Practical Adversarial Voice Recognition
ASR (automatic speech recognition) systems like Siri, Alexa, Google Voic...
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12/25/2017
