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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Bibliografische Detailangaben

Hauptverfasser:	Waruni Hewage, R. Sinha, M. Asif Naeem
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	SpringerOpen 2025-06-01
Schriftenreihe:	Journal of Big Data
Schlagworte:	Accuracy-privacy trade-off Data pertubation Logistic cumulative noise addition Privacy-preserving data mining
Online-Zugang:	https://doi.org/10.1186/s40537-025-01147-0
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