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Yang, Ying, Yang, Lina, Yang, Meihong, Yu, Huanhuan, Zhu, Guichun, Chen, Zhenya, Chen, Lijuan.  2019.  Dark web forum correlation analysis research. 2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC). :1216—1220.

With the rapid development of the Internet, the dark network has also been widely used in the Internet [1]. Due to the anonymity of the dark network, many illegal elements have committed illegal crimes on the dark. It is difficult for law enforcement officials to track the identity of these cyber criminals using traditional network survey techniques based on IP addresses [2]. The threat information is mainly from the dark web forum and the dark web market. In this paper, we introduce the current mainstream dark network communication system TOR and develop a visual dark web forum post association analysis system to graphically display the relationship between various forum messages and posters, and help law enforcement officers to explore deep levels. Clues to analyze crimes in the dark network.

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Li, Jian, Zhang, Zelin, Li, Shengyu, Benton, Ryan, Huang, Yulong, Kasukurthi, Mohan Vamsi, Li, Dongqi, Lin, Jingwei, Borchert, Glen M., Tan, Shaobo et al..  2019.  Reversible Data Hiding Based Key Region Protection Method in Medical Images. 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). :1526–1530.
The transmission of medical image data in an open network environment is subject to privacy issues including patient privacy and data leakage. In the past, image encryption and information-hiding technology have been used to solve such security problems. But these methodologies, in general, suffered from difficulties in retrieving original images. We present in this paper an algorithm to protect key regions in medical images. First, coefficient of variation is used to locate the key regions, a.k.a. the lesion areas, of an image; other areas are then processed in blocks and analyzed for texture complexity. Next, our reversible data-hiding algorithm is used to embed the contents from the lesion areas into a high-texture area, and the Arnold transformation is performed to protect the original lesion information. In addition to this, we use the ciphertext of the basic information about the image and the decryption parameter to generate the Quick Response (QR) Code to replace the original key regions. Consequently, only authorized customers can obtain the encryption key to extract information from encrypted images. Experimental results show that our algorithm can not only restore the original image without information loss, but also safely transfer the medical image copyright and patient-sensitive information.