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#Hider 2 vs encrypto license
TF Encrypted is open source community project developed under the Apache 2 license and maintained by a set of core developers. Privacy Preserving Deep Learning – PySyft Versus TF Encrypted makes a quick comparison between PySyft and TF Encrypted, correctly hitting on our goal of being the encryption backend in PySyft for TensorFlow ( by Exxact)īridging Microsoft SEAL into TensorFlow takes a first step towards integrating the Microsoft SEAL homomorphic encryption library and some of the technical challenges involved ( by Justin Patriquin at Cape Privacy) Privacy-Preserving Machine Learning in TensorFlow with TF Encrypted, O'Reilly AI 2019 ( by Morten Dahl at Cape Privacy) see also the slides Privacy-Preserving Machine Learning with TensorFlow, TF World 2019 ( by Jason Mancuso and Yann Dupis at Cape Privacy) see also the slides Private Machine Learning in TensorFlow using Secure Computation further elaborates on the benefits of the approach, outlines the adaptation of a secure computation protocol, and reports on concrete performance numbers ( by Morten Dahl, Jason Mancuso, Yann Dupis, et al.) Privacy-Preserving Collaborative Machine Learning on Genomic Data using TensorFlow outlines the iDASH'19 winning solution built on TF Encrypted ( by Cheng Hong, et al.)Ĭrypto-Oriented Neural Architecture Design uses TF Encrypted to benchmark ML optimizations made to better support the encrypted domain ( by Avital Shafran, Gil Segev, Shmuel Peleg, and Yedid Hoshen) Secure Computations as Dataflow Programs describes the initial motivation and implementation ( by Morten Dahl) Growing TF Encrypted outlines the roadmap and motivates TF Encrypted as a community project ( by Morten Dahl)Įxperimenting with TF Encrypted walks through a simple example of turning an existing TensorFlow prediction model private ( by Morten Dahl and Jason Mancuso at Cape Privacy) Introducing TF Encrypted walks through a simple example showing two data owners jointly training a logistic regression model using TF Encrypted on a vertically split dataset ( by Alibaba Gemini Lab)įederated Learning with Secure Aggregation in TensorFlow demonstrates using TF Encrypted for secure aggregation of federated learning in pure TensorFlow ( by Justin Patriquin at Cape Privacy)Įncrypted Deep Learning Training and Predictions with TF Encrypted Keras introduces and illustrates first parts of our encrypted Keras interface ( by Yann Dupis at Cape Privacy) We want to bring these on board and provide a bridge from TensorFlow. While TF Encrypted has its own implementations of secure computation, there are other excellent libraries out there for both secure computation and homomorphic encryption. This includes aligning with the upcoming TensorFlow 2.0 as well as figuring out how TF Encrypted can work closely together with related projects such as TF Privacy and TF Federated. So far TF Encrypted is focused on its low-level interface but it's time to figure out what it means for interfaces such as Keras when privacy enters the picture. High-level APIs for combining privacy and machine learning. reveal())įor more information, check out the documentation or the examples. # normal TensorFlow operations can be run locally # as part of defining a private input, in this # case on the machine of the input provider return tf. local_computation( 'input-provider') def provide_input(): Import tensorflow as tf import tf_encrypted as tfe tfe.