Privacy Preserving Parallel Distributed Data Stream Anonymization

Abstract

Sustainable stream processing algorithms have gained popularity in recent years. Flow control is a way of searching and modifying real-time data streams. Missing values are ubiquitous in real-world data streams, making data stream privacy challenging to safeguard. On the other hand, most privacy preservation methods need not take absent values into account when developed. They can anonymize data in certain study, however this results in data loss. This research proposes a unique parallel distributed approach for protecting privacy while using incomplete data streams. This method uses a production computational system to continually anonymize data streams, using clustering to construct each tuple. It clusters data in partial and complete forms using variable and array dimensions as similarity metrics. In order to prevent values and outliers’ pollution, a generalization approach that is based on more than matches is used. The experiments used real data to compare current systems with varied settings. This research will cover several anonymization mechanisms and their advantages. There are also drawbacks. Finally, we will explore the future of continuous data anonymization research.