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Found 6 papers, 3 papers with code
Privacy-Preserving Machine Learning: Methods, Challenges and Directions
In particular, existing PPML research cross-cut ML, systems and applications design, as well as security and privacy areas; hence, there is a critical need to understand state-of-the-art research, related challenges and a research roadmap for future research in PPML area.
FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Data
We empirically demonstrate the applicability for multiple types of ML models and show a reduction of 10%-70% of training time and 80% to 90% in data transfer with respect to the state-of-the-art approaches.
Blockchain-based Transparency Framework for Privacy Preserving Third-party Services
We also formally show the security guarantee provided by TAB, and analyze the privacy guarantee and trustworthiness it provides.
Cryptography and Security Networking and Internet Architecture
NN-EMD: Efficiently Training Neural Networks using Encrypted Multi-Sourced Datasets
We propose a novel framework, NN-EMD, to train DNN over multiple encrypted datasets collected from multiple sources.
HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning
Participants in a federated learning process cooperatively train a model by exchanging model parameters instead of the actual training data, which they might want to keep private.
CryptoNN: Training Neural Networks over Encrypted Data
To tackle the above issue, we propose a CryptoNN framework that supports training a neural network model over encrypted data by using the emerging functional encryption scheme instead of SMC or HE.
