# Detection of Sensitive Data to Counter Global Terrorism
## 1 Introduction
According to [35], [PERSON] and [PERSON] implemented TF-IDF and the Vector Space Model (VSM) to find topics in Arabic Dark websites, and the K-means clustering algorithm to group the terms, and they also implemented the concept of filtering, tokenization and stemming. In order to mine criminal information from online newspaper articles, [PERSON] et al. [36] employed Named Entity Recognition algorithms to identify thief-related information in sentences based on locations, and achieved 80-90% accuracy, but they had still employed a traditional method of computation. Most of this research adopted the numerical method of TF-IDF to determine the importance of words or to classify terrorism events, but a means for combining TF-IDF with data reduction techniques in order to reduce the size of the data and expose criminal information has yet to be reported. Dimensionality reduction is an important data processing technique [37] when processing huge amounts of data using singular value decomposition (SVD) [38]. The complex representation of higher dimensional data makes the decision space more complex and time-consuming in big text classification [39]. Therefore, SVD is one an important tool for generating different data sets by maintaining the originality of the information. In [40], [PERSON] et al. utilized SVD and the Principle Component Analysis (PCA) algorithm to feed compressed big data into the state estimation step, and showing that compressed data save time and speed up processing in distributed systems. To measure the difference between the original dataset and the distorted dataset for the degree of privacy protection, [PERSON] et al. [38] proposed a sparsified SVD for data distortion techniques. According to [41], [PERSON] et al. proposed an orthogonal tensor SVD method in the field of cybersecurity and cyber forensics, achieving promising results for higher-order big data reduction. In [10], [PERSON] et al. implemented two different machine learning algorithms--KNN and NN--to identify sensitive data from big data with Hadoop clusters, but they only considered the performance measures for mining the criminal information. [PERSON] and [PERSON] [42] presented an approach for identifying important words for sentiment classification using set operations and implementing truncated singular value decomposition and low-rank matrix decomposition techniques to retrieve documents in order to resolve issues related to noise removal and synonymy. Based on this research, we can see that SVD has been implemented for the reduction of the size of voluminous data in order to feed compressed big data into the state estimation, to measure the difference between the original dataset and the distorted dataset, and to resolve issues related to noise removal, but there exists a big literature gap with respect to identifying accurate criminal data in big data using Hadoop clusters for combating global terrorism. According to the above studies, we argue that the existing approaches are insufficient to account for the exponential growth of data, and we also find that the traditional computational methods are ineffective in the retrieval of criminal data from voluminous data. Moreover, it is also argued that the algorithmic approach should be implemented with the Hadoop cluster to achieve higher performance for the retrieval of sensitive data. Based on these arguments, this research is aimed at the accurate identification of criminal data from the huge mass of varieties of data collected from diversified fields through the use of Hadoop clusters to support the criminal justice system and social justice organizations in fighting terrorist activities at a global scale. The achievement of this experiment is rigorously compared using the same set of hardware, software, and system configuration. Moreover, the efficacy of the experiment was tested using criminal data with respect to concepts and matching scores.