Machine Unlearning: A Novel Framework to Unlearning, Privacy and Defending Against Inference Attacks

Research
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Hey everyone,

I am excited to present my latest venture, an initiative aimed at exploring the still-murky waters of Machine Unlearning. While this new project shares its roots with our previous endeavors in biomimetic machine learning, it diverges to concentrate on the fascinating and complex issue of algorithmic forgetfulness.

:dart: Objective

The cornerstone of this project is not just to create algorithms that can forget, but to do so in a way that’s both efficient and secure. Our vision transcends mere algorithmic performance, embracing a multi-faceted approach that also covers privacy protections and robust defenses against model inference attacks. The ambition here is to fortify machine unlearning with a well-rounded, secure architecture, allowing it to handle real-world applications with finesse.

:bookmark_tabs: Methodological Approach

Conceptual Framework: At the core of our initiative is a conceptual framework that, although drawing inspiration from biomimicry, focuses predominantly on the facets of machine unlearning. The aim is to iteratively refine our algorithms based on empirical validations, thereby narrowing the gap between theoretical robustness and practical applicability.

Prototypes:

  • Focused Unlearning Notebook: This prototype serves as our experimental bedrock. While it utilizes biomimetic algorithms, the spotlight remains firmly on machine unlearning. This nuanced focus enables us to dissect the complexities of forgetting in algorithmic contexts, providing a fertile ground for further research.

:microscope: Preliminary Outcomes

  • Attack Accuracy: Initial evaluations conducted with Membership Inference Attacks (MIA) have shown that our unlearning models hold their ground as effectively as traditional models, a promising sign for their robustness and security.

  • Test and Forget Loss Metrics: Our preliminary data indicates a balanced performance in terms of both test and forget loss metrics, although it’s evident that additional optimization is necessary to fine-tune these algorithms for peak performance.

:eye: An Invitation for Rigorous Academic Examination

We’re at the inception of this research and are wholeheartedly welcoming of rigorous academic scrutiny. We are particularly interested in:

  • Peer reviews that dive deep into the mathematical formulations and real-world applicability of our unlearning algorithms.

  • Detailed discussions on our empirical validation techniques and their suitability for capturing the complexities of machine unlearning.

  • Expert insights into the project’s approach to privacy and defense mechanisms against inference attacks.

:open_file_folder: Access to All Research Artifacts

For those interested in delving deeper, all our code, Jupyter notebooks, and extensive documentation are accessible in the GitHub repository: GitHub - severian42/Machine-Unlearning

If you’d like to try out our focused unlearning algorithm, the notebook is available here: Google Colab

Your insights, critiques, and questions are not just welcome; they’re essential for the evolution of this experimental research. Thanks for checking it out!