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About Deep Reinforcement Learning-Based Text Anonymization Against Private-Attribute Inference
1) The document proposes a novel Reinforcement Learning-based Text Anonymizer (RLTA) to anonymize user-generated text data against private attribute inference while preserving data utility for downstream tasks. 2) RLTA first extracts a latent task-aware representation of the original text, then uses deep reinforcement learning to learn an optimal strategy for manipulating the text representation based on privacy and utility feedback. 3) Experiments on real-world data demonstrate RLTA can effectively preserve both privacy and utility of text embeddings during the anonymization process.
Detailed Information
| Author: | ['Duarte Harris Cruz'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 10 |
| Language: | English |
| Format: | |
| Price: | FREE |
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