Biblio

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.

Embedded Systems (ES) underlie society's critical cyberinfrastructure and comprise the vast majority of consumer electronics, making them a prized target for dangerous malware and hardware Trojans. Malicious intrusion into these systems present a threat to national security and economic stability as globalized supply chains and tight network integration make ES more susceptible to attack than ever. High-end ES like the Xilinx Zynq-7020 system on a chip are widely used in the field and provide a representative platform for investigating the methods of cybercriminals. This research suggests a novel anomaly detection framework that could be used to detect potential zero-day exploits, undiscovered rootkits, or even maliciously implanted hardware by leveraging the Zynq architecture and real-time device-level measurements of thermal side-channels. The results of an initial investigation showed different processor workloads produce distinct thermal fingerprints that are detectable by out-of-band, digital logic-based thermal sensors.