SECURE AND PRIVATE AI SCHOLARSHIP CHALLENGE

Notebooks for Udacity's Secure and Private AI course.

IDEA

PART - I

THE WHOLE IDEA OF DIFFERENTIAL PRIVACY MECHANISM IS TO BLOCK OUT OR FILTER OUT SPECIFIC FEATURES PRIVATE TO SPECIFIC INDIVIDUALS AND ALLOW THE LEARNING PROCESS TO LEARN MORE ABOUT GENERAL CHARACTERSTICS OR FEATURES WHICH IS SAME ACROSS MOST OF THE DATAPOINTS OF THE DATASET. THIS PREVENTS THE NEURONS OF THE NEURAL NETWORK TO LEARN THE INDIVIDUAL METRICS OF ANY PERSON AND THUS LEARN OVERALL METRICS REQUIRED FOR PREDICTION. THERE IS A TRADE-OFF BETWEEN PRIVACY PROTECTION AND ACCURACY. TO IMPLMENET PRIVACY WE ADD NOISE (GAUSSIAN OR LAPLACIAN , TO INPUT OR OUTPUT) AND THERE ARE TWO WAYS TO IMPLEMENT THIS -

LOCAL DIFFERENTIAL PRIVACY WHERE WE ADD NOISE TO THE INPUT OF THE DATASET SO THAT CURATOR DOESN'T KNOWS THE EXACT VALUES OF THE ELEMENTS IN THE DATABASE.
GLOBAL DIFFERENTIAL PRIVACY WHERE THE CURATOR HAS THE RESPONSIBILITY TO ADD THE NOISE AFTER THE QUERY FUNCTION IS RUN OVER THE DATABASE.. IT'S ALL BASED ON WHO TRUSTS WHOM. ALL THE DETAILED CONCEPTS HAS BEEN INCLUDED IN THE NOTEBOOKS COMMENTS SECTION ITSELF. FINALLY WE WILL SEE HOW TO IMPLEMENT DIFFERENTIAL PRIVACY IN DEEP LEARNING MODELS.

PART - II

WE IMPLEMENT FEDERATED LEARNING

PART - III

WE IMPLEMENTED SECURED FEDERATED LEARNING PROTOCOL

PART - IV

WE IMPLEMENT ENCRYPTION ON DEEP LEARNING MODEL

PART - V

FINALLY WE IMPLEMENT KEYSTONE PROJECT COMBINING ALL LEARNINGS FROM THE COURSE.

Dependencies

To run these notebooks you'll need to install Python 3.6+, PySyft, Numpy, PyTorch 1.0, and Jupyter Notebooks. The easiest way for all of this is to create a conda environment:

conda create -n pysyft python=3
conda activate pysyft
conda install numpy jupyter notebook
conda install pytorch torchvision -c pytorch
pip install syft

SOME OF THE NOTEBOOKS ARE OF ORIGINAL UDACITY'S COURSE AND OTHERS ARE MADE BY ME DURING LEARNING OF THE COURSE. THE COMPLETED FOLDER CONTAINS ANDREW'S SOLUTIONS AND THE OTHER FILES ARE MY OWN SOLUTIONS/IMPLEMENTATIONS. ALL THE CODES ARE COMMENTED FOR DETAILED EXECUTION-FLOW OF THE PROGRAMS.

ALSO THERE IS THIS #60DAYSOFUDACITY WHERE I HAVE PLEDGED TO CODE FOR 60 DAYS AND ALSO DO SOME PROJECTS RELATED TO THIS COURSE. #udacityfacebookscholar @sourav kumar

Name		Name	Last commit message	Last commit date
Latest commit History 92 Commits
.ipynb_checkpoints		.ipynb_checkpoints
DEEP LEARNING MODELS PRACTICE		DEEP LEARNING MODELS PRACTICE
completed		completed
60daysofudacity.JPG		60daysofudacity.JPG
Encrypted_deep_learning_final_toy_dataset_training.ipynb		Encrypted_deep_learning_final_toy_dataset_training.ipynb
Final_PATE_project.ipynb		Final_PATE_project.ipynb
Final_encrypted_dl_project_MNIST.ipynb		Final_encrypted_dl_project_MNIST.ipynb
Final_federated_learning_project.ipynb		Final_federated_learning_project.ipynb
Final_secured_fed_securing.ipynb		Final_secured_fed_securing.ipynb
Keystone_project.ipynb		Keystone_project.ipynb
PATE_ANALYSIS_AND_IMPLEMENTATION.ipynb		PATE_ANALYSIS_AND_IMPLEMENTATION.ipynb
README.md		README.md
Section 1 - Differential Privacy.ipynb		Section 1 - Differential Privacy.ipynb
Section 2 - Federated Learning.ipynb		Section 2 - Federated Learning.ipynb
Section 3 - Securing Federated Learning.ipynb		Section 3 - Securing Federated Learning.ipynb
Section 4 - Encrypted Deep Learning.ipynb		Section 4 - Encrypted Deep Learning.ipynb
Section 4b - Encrypted Keras Client.ipynb		Section 4b - Encrypted Keras Client.ipynb
adding_laplacian_noise_global_diff_privacy.ipynb		adding_laplacian_noise_global_diff_privacy.ipynb
annotating_unlabelled_dataset_using_dp_query.ipynb		annotating_unlabelled_dataset_using_dp_query.ipynb
differential_privacy_attack.ipynb		differential_privacy_attack.ipynb
differential_privacy_private_dbs.ipynb		differential_privacy_private_dbs.ipynb
differential_privacy_sensitvity1.ipynb		differential_privacy_sensitvity1.ipynb
dp_formula.png		dp_formula.png
encrypted_deep_learning_addition_subtraction_scalar_multiplication.ipynb		encrypted_deep_learning_addition_subtraction_scalar_multiplication.ipynb
encrypted_deep_learning_in_pysyft.ipynb		encrypted_deep_learning_in_pysyft.ipynb
federated_learning_advanced_remote_tensoroperations.ipynb		federated_learning_advanced_remote_tensoroperations.ipynb
federated_learning_learning_a_simple_model.ipynb		federated_learning_learning_a_simple_model.ipynb
federated_learning_project_remote_arithmetic.ipynb		federated_learning_project_remote_arithmetic.ipynb
noise_and_without_noise_db_analysis.ipynb		noise_and_without_noise_db_analysis.ipynb
random_noise_analysis_differential_private_ai.ipynb		random_noise_analysis_differential_private_ai.ipynb
secureandprivateai.JPG		secureandprivateai.JPG
secured_federated_learning.ipynb		secured_federated_learning.ipynb
short-conv-mnist.h5		short-conv-mnist.h5
udacity-logo-vert-white.png		udacity-logo-vert-white.png

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SECURE AND PRIVATE AI SCHOLARSHIP CHALLENGE

IDEA

PART - I

PART - II

PART - III

PART - IV

PART - V

Dependencies

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