Efficient Approximation and Privacy Preservation Algorithms for real time online Evolving Data Streams

Abstract

Abstract Mining real-time streaming data is a more difficult research challenge than mining static data due to the processing of continuous unstructured massive streams of data. As sensitive data is incorporated into the streaming data, the issue of privacy continues. In recent years, there has been significant progress in research on the anonymization of static data. For the anonymization of quasi-identifiers, two typical strategies are generalization and suppression. But the high dynamicity and potential infinite properties of the streaming data make it a challenging task. To end this, we propose a novel Efficient Approximation and Privacy Preservation Algorithms (EAPPA) framework in this paper to achieve efficient data pre-processing from the live streaming and its privacy preservation with minimum Information Loss (IL) and computational requirements. As the existing privacy preservation solutions for streaming data suffered from the challenges of redundant data, we first proposed the efficient technique of data approximation with data pre-processing. We design the Flajolet Martin (FM) algorithm for robust and efficient approximation of unique elements in the data stream with a data cleaning mechanism. We fed the periodically approximated and pre-processed streaming data to the anonymization algorithm. We propose novel k-anonymization and l-diversity privacy principles for data streams using adaptive clustering. The proposed approach scans a stream to detect and reuse clusters that fulfill the k-anonymity and l-diversity criteria for reducing anonymization time and IL. The experimental results reveal the efficiency of the EAPPA framework compared to state-of-art methods.