چكيده به لاتين
Big Data is a term used for very large and complex datasets. These characteristics bring with them challenges in how to store, analyze, and apply existing traditional methods and extract results. With the significant growth of data in recent years, privacy has become one of the main concerns in this area; However, there is always a trade off between the privacy and security of big data with its widespread use. To solve this problem, various models and algorithms have been developed, but most of them suffer from one problem; Rapid growth of data dimensions. Therefore, traditional algorithms and methods are not responsive to this type of data.
Today, streaming data plays a very important role in the world around us. Healthcare, financial markets, and the Internet of Things are examples of areas that are highly dependent on streaming data. The privacy of this data is challenging because this type of data is not static unlike the data stored in databases. The two components of anonymous data quality and data age are important in streaming data. Maintaining the utility of this data which means increasing the amount of the first component and decreasing the amount of the second one, is the main challenge of today's algorithms and methods. Represnting a way to achieve this important; In this area, is very instructive and practical.
In this dissertation, a method is presented whose main purpose is to maintain the utility of streaming data. Also, providing a platform and infrastructure to make the system scalable, has made it more adaptable to very large input data. To evaluate the proposed method, two variables have been used: the average information loss rate and the average data delay rate, which represent the first and second components of the stream data utility, respectively. The results obtained by comparing the proposed method with three prominent methods in this field, show that the efficiency of this method is much better and more than others.
