An Assessment of the Application of Private Aggregation of Ensemble Models to Sensible Data
This paper explores the use of Private Aggregation of Teacher Ensembles (PATE) in a setting where students have their own private data that cannot be revealed as is to the ensemble. We propose a privacy model that introduces a local differentially private mechanism to protect student data. We implem...
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| Үндсэн зохиолч: | |
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| Бусад зохиолчид: | , , |
| Формат: | article |
| Хэл сонгох: | англи |
| Хэвлэсэн: |
2021
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| Нөхцлүүд: | |
| Онлайн хандалт: | https://hdl.handle.net/20.500.12381/456 https://doi.org/10.3390/make3040039 |
| Шошгууд: |
Шошго байхгүй, Энэхүү баримтыг шошголох эхний хүн болох!
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| Тойм: | This paper explores the use of Private Aggregation of Teacher Ensembles (PATE) in a setting where students have their own private data that cannot be revealed as is to the ensemble. We propose a privacy model that introduces a local differentially private mechanism to protect student data. We implemented and analyzed it in case studies from security and health domains, and the result of the experiment was twofold. First, this model does not significantly affecs predictive capabilities, and second, it unveiled interesting issues with the so-called data dependency privacy loss metric, namely, high variance and values. |
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