Facebook Report on Privacy of fNIRS data
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866916079308111872 |
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| author | Hossen, Md Imran Chilukoti, Sai Venkatesh Shan, Liqun Tida, Vijay Srinivas Hei, Xiali |
| author_facet | Hossen, Md Imran Chilukoti, Sai Venkatesh Shan, Liqun Tida, Vijay Srinivas Hei, Xiali |
| contents | The primary goal of this project is to develop privacy-preserving machine learning model training techniques for fNIRS data. This project will build a local model in a centralized setting with both differential privacy (DP) and certified robustness. It will also explore collaborative federated learning to train a shared model between multiple clients without sharing local fNIRS datasets. To prevent unintentional private information leakage of such clients' private datasets, we will also implement DP in the federated learning setting. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_00973 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Facebook Report on Privacy of fNIRS data Hossen, Md Imran Chilukoti, Sai Venkatesh Shan, Liqun Tida, Vijay Srinivas Hei, Xiali Machine Learning Cryptography and Security I.2.0 The primary goal of this project is to develop privacy-preserving machine learning model training techniques for fNIRS data. This project will build a local model in a centralized setting with both differential privacy (DP) and certified robustness. It will also explore collaborative federated learning to train a shared model between multiple clients without sharing local fNIRS datasets. To prevent unintentional private information leakage of such clients' private datasets, we will also implement DP in the federated learning setting. |
| title | Facebook Report on Privacy of fNIRS data |
| topic | Machine Learning Cryptography and Security I.2.0 |
| url | https://arxiv.org/abs/2401.00973 |