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https://github.com/dahmansphi/differential_privacy_with_ai_and_ml

Review of Data Privacy Techniques: Concepts, Scenarios & Architectures, Simulations, Challenges, and Future Directions
https://github.com/dahmansphi/differential_privacy_with_ai_and_ml

ai data-privacy differential-privacy machine-learning

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Review of Data Privacy Techniques: Concepts, Scenarios & Architectures, Simulations, Challenges, and Future Directions

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README 

          
![Screenshot of build-in functions of the protectai tool.](https://raw.githubusercontent.com/dahmansphi/differential_privacy_with_ai_and_ml/main/assets/privacy_upd.jpg)



 

> [!TIP]

> This project is the technical implementation of the published paper [Review of Data Privacy Techniques: Concepts, Scenarios and Architectures, Simulations, Challenges, and Future Directions](https://dahmansphi.com/publications/).



> [!TIP]

> The Lab associated with this project can be found at the provided repository [link](https://github.com/dahmansphi/differential_privacy_with_ai_and_ml/blob/main/lab/diff_privacy.ipynb)



> [!TIP]

> __Availability of supporting data__: The following data support the findings of this study: The Heart Failure Clinical Records dataset available in [UCI Machine Learning Repository] at https://doi.org/10.24432/C5Z89R ,



> [!TIP]

> __Training Videos__: You can watch a concise series of training videos that demonstrate each section of the published academic paper, with the author delivering the content. [Training Program](https://www.youtube.com/playlist?list=PLhej4kLpU4j1hwYLVCjQYeGvnTUaJ7_JG)



# About the Project

## Author's Words

Welcome to the official documentation for **Review of Data Privacy Techniques: Concepts, Scenarios and Architectures, Simulations, Challenges, and Future Directions**. I am Dr. Deniz Dahman, the creator of the BireyselValue algorithm and the author of this project. The following section will provide a brief introduction to the core idea of the __project__, along with a reference to the academic publication. I would like to inform you that this work was conducted independently, without any funding or similar support. 



I am committed to continuing and seeking further enhancements to the review paper. Should you wish to contribute to this work in any manner, please refer to the details in the contributing section.  

  

## Contributing 



If you're interested in supporting the creator and author of this project, you might consider exploring the various available options for contribution.



> `To Contribute in any way possible, thank you, you can check` :



1. view options to subscribe on [Dahman's Phi Services Website](https://dahmansphi.com/subscriptions/)

2. subscribe to this channel [Dahman's Phi Services](https://www.youtube.com/@dahmansphi)     

3. you can support on [patreon](https://patreon.com/user?u=118924481) 



If you prefer *any other way of contribution*, please feel free to contact me directly on [contact](https://dahmansphi.com/contact/). 



*Thank you*



# The Abstract



This review offers __a thorough introduction to the concept of privacy, focusing specifically on the application of differential privacy within machine learning__. Its primary goal is to elucidate the key facets of privacy when utilizing AI frameworks, thereby _facilitating researchers and students in gaining a coherent understanding from a singular resource_. Additionally, the project provides __lab implementations in Python for several key topics__. In essence, apart from presenting available open-source tools for privacy in machine learning, the author has chosen to demonstrate the core concepts of the abstract within a hypothetical setup environment. While the review does not guarantee mastery over every discussed topic, it aims to provide a substantial comprehension, enabling the reader to further explore specific areas related to the discourse on privacy in machine learning. The project is structured into sections as outlined below.



# Section 2:

This section focuses on three core elements: data, AI frameworks, and privacy. __It seeks to establish a link between these components__, leading to the main emphasis—data privacy in the context of machine learning and AI frameworks.



# Section 3:

Exploring the diverse techniques and methods for applying data privacy across various scenarios can be aided by numerous open-source tools. However, this project adopts a unique approach. __Rather than merely introducing the abstract concept and then recommending an existing tool for implementation, it constructs lab sessions using the Python programming language__. In each subsession, it builds upon the core principles of the abstract concept from the ground up and then simulates the results. This method ensures that the reader gains a solid understanding of both the mathematical and procedural aspects of the topic.

The lab session will utilize a single dataset mentioned in the `data availability`; importantly, the nature of the dataset, including its field of origin or the types of features it contains, will not be a concern. This is because the paper intends to use the dataset solely as an illustrative example, and consequently, there will be no evaluation of outcome metrics such as accuracy, efficiency, or the like



# Section 4:

The discussions in the previous sections (1 and 2) have underscored the significance of embedding privacy as a core element in the use of data for general analytics or predictive modeling within AI and ML frameworks. __This section will present detailed, motivating examples__ related to data privacy issues, followed by a laboratory simulation of a data privacy breach.



# Section 5:

As discussions around data privacy have intensified in recent years, _numerous approaches have been explored to address the issue from both theoretical and practical perspectives_. While these techniques may seem disparate at first glance, they are interconnected to some degree. _Differential Privacy stands out as a significant mathematical concept in this domain_. __This section will present the latest techniques for addressing data privacy concerns__. Importantly, the arrangement of these techniques is not presented as isolated topics but as a continuum of related subjects. Consequently, the ensuing subsections and simulated laboratories will be discussed in a logical sequence



# Section 6:

Despite the promising mathematical representation of __differential privacy__, there are still challenges in the field today. This section discuss those challenges in details.



# Section 7:

This project conducted __an extensive survey on data privacy__. It provided a wide-ranging viewpoint, linking data, AI & ML frameworks, and privacy concerns. The review outlined two scenarios where privacy is compromised and explored different initiatives to tackle data privacy issues. __A notable aspect of this work was the in-depth analysis of the "Differential Privacy" framework__, a leading subject in current privacy research. Challenges and future prospects regarding the Differential Privacy (DP) framework were also examined. It has become evident that incorporating a privacy parameter into data usage for any purpose is crucial. The existing methods and frameworks, particularly the differential privacy framework, appear to be quite promising. It is anticipated that this framework will continue to evolve in the coming years and may become the new benchmark for data privacy. __Regardless of the advancements a framework makes, without public knowledge and understanding of privacy technicalities, which is difficult to expect from non-technical individuals, addressing privacy breaches becomes an almost unattainable goal__. This is because if people remain uninformed about the technical aspects of privacy concerning their data, they have no choice but to trust the curator's assurance that their privacy is intact. While this may be true, the question arises: if the curator uses the data entrusted to them without the explicit consent of the owner for their own benefit, can it be considered a breach of privacy? Lastly, this overview serves as a foundation for the "data privacy" community interested in developing various techniques for data protection. Moreover, it enables researchers to determine the most appropriate course of action to offer more precise alternatives in the field



# Reference



please follow up on the [publications](https://dahmansphi.com/publications/) on the website to find the academic [published paper](https://www.scienceopen.com/hosted-document?doi=10.14293/PR2199.000936.v1)