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...
I tiakina i:
| Ngā kaituhi matua: | , , |
|---|---|
| Hōputu: | Artigo |
| Reo: | Inglês |
| I whakaputaina: |
SpringerOpen
2025-06-01
|
| Rangatū: | Journal of Big Data |
| Ngā marau: | |
| Urunga tuihono: | https://doi.org/10.1186/s40537-025-01147-0 |
| Ngā Tūtohu: |
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
|