ZumrutMuftuoglu

ZumrutMuftuoglu's Stars

• usnistgov/ESV-Server

  Entropy Source Validation Protocol and Server specifications

  Language:Python1312

• OpenMined/PipelineDP

  PipelineDP is a Python framework for applying differentially private aggregations to large datasets using batch processing systems such as Apache Spark, Apache Beam, and more.

  Language:Python27477

• International-Data-Spaces-Association/idsa

  This is the main repository of International Data Spaces Association on GitHub, where you can find general overview and useful information on IDS Landscape.

  9529

• whopriyam/Benchmarking-Differential-Privacy-and-Federated-Learning-for-BERT-Models

  Language:Jupyter Notebook162

• riacheruvu/neural-cryptography

  An experimental neural cryptography algorithm created in 2016 that implements convolutional neural networks for cryptography based on others' attempts.

  Language:Jupyter Notebook152

• jeremiahbonney/Neural-Key-Exchange-Protocol

  Proof of concept dealing with NKEP written in Python. If you don't know what NKEP is, see http://cryptome.org/neuralsub.ps for more info and look up Tree Parity Machines on Wikipedia.

  Language:Python3

• farizrahman4u/neuralkey

  Neural Key Exchange using Tree Parity Machine in Python

  Language:Python197

• Harkirat155/deeplog

  Language:Python2

• tango4j/tensorflow-vs-pytorch

  Guide for both TensorFlow and PyTorch in comparative way

  Language:Jupyter Notebook10918

• craffel/dl3d-seminar

  Course webpage for COMP 790, (Deep) Learning from Limited Labeled Data

  30229

• usnistgov/PrivacyEngCollabSpace

  Privacy Engineering Collaboration Space

  Language:Python235129

• OpenMined/writing

  115

• ayyucekizrak/Udemy_DerinOgrenmeyeGiris

  Udemy Derin Öğrenmeye Giriş Kursunun Uygulamaları ve Daha Fazlası

  Language:Jupyter Notebook362194

• Lory94/dp-GAN

  Differentially Private Generative Adversarial Networks for Time Series, Continuous, and Discrete Open Data

  334

• nesl/nist_differential_privacy_synthetic_data_challenge

  UCLANesl - NIST Differential Privacy Challenge (Match 3)

  Language:Python2310

• OpenMined/TenSEAL

  A library for doing homomorphic encryption operations on tensors

  Language:C++811156

• yz-ai/acikseminer2020

  Açık Seminer (https://www.acikseminer.com/) serisinin doğal dil işleme haftasındaki 14. günündeki NLP 101: Doğal Dil İşlemeye Giriş ve 20. günündeki Derin Öğrenme ile Göğüs Röntgeni Görüntülerinden Tanı Önerisi Çalışması'na ait sunum ve kaynakları içerir

  Language:Jupyter Notebook488

• OpenMined/PyDP

  The Python Differential Privacy Library. Built on top of: https://github.com/google/differential-privacy

  Language:Python503139

• ebagdasa/backdoor_federated_learning

  Source code for paper "How to Backdoor Federated Learning" (https://arxiv.org/abs/1807.00459)

  Language:Python27062

• ebagdasa/differential-privacy-vs-fairness

  Code for "Differential Privacy Has Disparate Impact on Model Accuracy" NeurIPS'19

  Language:Python3413

• menisadi/awesome-differential-privacy

  list of differential-privacy related resources

  28143

• pranjalchaubey/Deep-Learning-Notes

  My personal notes, presentations, and notebooks on everything Deep Learning.

  Language:Jupyter Notebook22751

• udacity/CVND_Exercises

  Exercise notebooks for CVND.

  Language:Jupyter Notebook761687

• jnear/cs211-data-privacy

  Language:Jupyter Notebook4445

• XintongHao/Robust-CNN-with-Differential-Privacy

  Research paper: https://arxiv.org/abs/1802.03471.

  Language:Jupyter Notebook6

• thienan092/60-Days-Of-Udacity

  A challenge in Secure & Private AI Scholarship Challenge.

  Language:Jupyter Notebook63

• OpenMined/PySyft

  Perform data science on data that remains in someone else's server

  Language:Python9.5k2k

• udacity/private-ai

  Repo for Udacity's Secure & Private AI course

  Language:Jupyter Notebook449394

• udacity/deep-learning-v2-pytorch

  Projects and exercises for the latest Deep Learning ND program https://www.udacity.com/course/deep-learning-nanodegree--nd101

  Language:Jupyter Notebook5.3k5.3k