Summary of Facebook Report on Privacy Of Fnirs Data, by Md Imran Hossen et al.
Facebook Report on Privacy of fNIRS data
by Md Imran Hossen, Sai Venkatesh Chilukoti, Liqun Shan, Vijay Srinivas Tida, Xiali Hei
First submitted to arxiv on: 1 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This project aims to develop privacy-preserving machine learning techniques for functional near-infrared spectroscopy (fNIRS) data. The approach involves training a local model in a centralized setting using differential privacy (DP) and certified robustness. Additionally, the project will explore collaborative federated learning to train a shared model between multiple clients without sharing their private fNIRS datasets. To prevent unintentional leakage of sensitive information, DP will also be implemented in the federated learning setting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This project wants to keep personal brain activity data safe when training machine learning models. They’re trying new ways to do this using special privacy techniques. One way is to train a model on the same information everyone has, so nobody can figure out what individual people’s brain data looks like. Another way is to let different people have their own models and combine them without sharing any personal data. |
Keywords
* Artificial intelligence * Federated learning * Machine learning
