8 citations · 15 across the 4 of their papers we have counts for
4 papers
MOFHEI: Model Optimizing Framework for Fast and Efficient Homomorphically Encrypted Neural Network Inference
Parsa Ghazvinian, Robert Podschwadt, Prajwal Panzade +2
Due to the extensive application of machine learning (ML) in a wide range of fields and the necessity of data privacy, privacy-preserving machine learning (PPML) solutions have rec…
SoK: Privacy-preserving Deep Learning with Homomorphic Encryption
Robert Podschwadt, Daniel Takabi, Peizhao Hu
Outsourced computation for neural networks allows users access to state of the art models without needing to invest in specialized hardware and know-how. The problem is that the us…
Privacy preserving Neural Network Inference on Encrypted Data with GPUs
Daniel Takabi, Robert Podschwadt, Jeff Druce +2
Machine Learning as a Service (MLaaS) has become a growing trend in recent years and several such services are currently offered. MLaaS is essentially a set of services that provid…
Effectiveness of Adversarial Examples and Defenses for Malware Classification
Robert Podschwadt, Hassan Takabi
Artificial neural networks have been successfully used for many different classification tasks including malware detection and distinguishing between malicious and non-malicious pr…