This study addresses privacy concerns in real-time streaming data, including personal biometric signals and private information from sources such as real-time crime reporting, online sales transactions, and hospital patient-monitoring devices. Anonymization is crucial because it hides sensitive personal data. Achieving anonymity in real-time streaming data involves satisfying the unique demands of real-time scenarios, which is distinct from traditional methods. Specifically, security and minimal information loss must be maintained within a specified timeframe (referred to as the average delay time). The most recent solution in this context is the utility-based approach to data stream anonymization (UBDSA) algorithm developed by Sopaoglu and Abul. This study aims to enhance the performance of UBDSA by introducing a secure, useful, and high-performance data stream anonymization (SUHDSA) algorithm. SUHDSA outperforms UBDSA in terms of runtime and information loss while still ensuring privacy protection and an average delay time. The experimental results, using the same dataset and cluster size as in a previous UBDSA study, demonstrate significant performance improvements with the proposed algorithm. It achieves a minimum runtime of 24.05 s and a maximum runtime of 29.88 s, with information loss rates ranging from 14% to 77%. These results surpass the performance of the previous UBDSA algorithm.