An accuracy-privacy optimization framework considering user’s privacy requirements for data stream mining
Abstract Data stream mining is a critical process utilized by organizations to derive insights from real-time data. Consequently, preserving the privacy of sensitive information while maintaining high accuracy remains a persistent challenge. Privacy-preserving data mining techniques modify data to i...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2025-06-01
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| Series: | Journal of Big Data |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40537-025-01147-0 |
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