A State-of-the-Art Review of Data Stream Anonymization Schemes
Aderonke Busayo Sakpere, Anne V. D. M. Kayem · Advances in information security, privacy, and ethics book series · 2014
Streaming data emerges from different electronic sources and needs to be processed in real time with minimal delay. Data streams can generate hidden and useful knowledge patterns when mined and analyzed. In spite of these benefits, the issue of privacy needs to be addressed before streaming data is released for mining and analysis purposes. In order to address data privacy concerns, several techniques have emerged. K-anonymity has received considerable attention over other privacy preserving techniques because of its simplicity and efficiency in protecting data. Yet, k-anonymity cannot be directly applied on continuous data (data streams) because of its transient nature. In this chapter, the authors discuss the challenges faced by k-anonymity algorithms in enforcing privacy on data streams and review existing privacy techniques for handling data streams.