On Effectiveness of Adversarial Examples and Defenses for Malware Classification. - Robert Podschwadt; Hassan Takabi (SecureComm (2), 2019)

Privacy preserving Neural Network Inference on Encrypted Data with GPUs. - Daniel Takabi; Robert Podschwadt; Jeff Druce; Curt Wu; Kevin Procopio (CoRR, 2019)

Classification of Encrypted Word Embeddings using Recurrent Neural Networks. - Robert Podschwadt; Daniel Takabi (PrivateNLP@WSDM, 2020)

Non-interactive Privacy Preserving Recurrent Neural Network Prediction with Homomorphic Encryption. - Robert Podschwadt; Daniel Takabi (CLOUD, 2021)

SoK: Privacy-preserving Deep Learning with Homomorphic Encryption. - Robert Podschwadt; Daniel Takabi; Peizhao Hu (CoRR, 2021)

A Survey of Deep Learning Architectures for Privacy-Preserving Machine Learning With Fully Homomorphic Encryption. - Robert Podschwadt; Daniel Takabi; Peizhao Hu; Mohammad Hossein Rafiei; Zhipeng Cai 001 (IEEE Access, 2022)

NeuroCrypt: Machine Learning Over Encrypted Distributed Neuroimaging Data. - Nipuna Senanayake; Robert Podschwadt; Daniel Takabi; Vince D. Calhoun; Sergey M. Plis (Neuroinformatics, 2022)

Memory Efficient Privacy-Preserving Machine Learning Based on Homomorphic Encryption. - Robert Podschwadt; Parsa Ghazvinian; Mohammad GhasemiGol; Daniel Takabi (ACNS (2), 2024)

MOFHEI: Model Optimizing Framework for Fast and Efficient Homomorphically Encrypted Neural Network Inference. - Parsa Ghazvinian; Robert Podschwadt; Prajwal Panzade; Mohammad Hossein Rafiei; Daniel Takabi (TPS-ISA, 2024)