Over the past several decades, digital information science has emerged to seek answers to the question: can any technique ensure tamper-resistance and protect the copyright of digital contents by storing, transmitting and processing information encoded in systems where digital content can easily be disseminated through communication channels? Today it is understood that the answer is yes. This paper reviews the theoretical analysis and performance investigation of representative watermarking systems in transform domains and geometric invariant regions. Digital watermarking is a technology of embedding watermark with intellectual property rights into images, videos, audios, and other multimedia data by a certain algorithm. The basic characteristics of digital watermark are imperceptibility, capacity, robustness and false positive of watermarking algorithm and security of the hiding place. Moreover, it is concluded that various attacks operators are used for the assessment of watermarking systems, which supplies an automated and fair analysis of substantial watermarking methods for chosen application areas

There are tremendous security concerns with patient health monitoring sensors in Internet of Things (IoT). The concerns are also realized by recent sophisticated security and privacy attacks, including data breaching, data integrity, and data collusion. Conventional solutions often offer security to patients’ health monitoring data during the communication. However, they often fail to deal with complicated attacks at the time of data conversion into cipher and after the cipher transmission. In this paper, we first study privacy and security concerns with healthcare data acquisition and then transmission. Then, we propose a secure data collection scheme for IoT based healthcare system named SecureData with the aim to tackle security concerns similar to the above. SecureData scheme is comprised of four layers: IoT network sensors/devices, Fog layers, Cloud computing layer, and healthcare provider layer. We mainly contribute to the first three layers. For the first two layers, SecureData includes two techniques: light-weight FPGA hardware-based cipher algorithm and secret cipher share algorithm, respectively. We study KATAN algorithm and we implement and optimize it on the FPGA hardware platform, while we use the idea of secret cipher sharing technique to protect patients’ data privacy. At the cloud computing layer, we apply a distributed database technique that includes a number of cloud data servers to guarantee patients’ personal data privacy at the cloud computing layer. The performance of SecureData is validated through simulations with FPGA in terms of hardware frequency rate, energy cost, and computation time of all the algorithms and the results show that SecureData can be efficient when applying for protecting security risks in IoT-based healthcare.

In the past few decades, the rise in attacks on communication devices in networks has resulted in a reduction of network functionality, throughput, and performance. To detect and mitigate these network attacks, researchers, academicians, and practitioners developed Intrusion Detection Systems (IDSs) with automatic response systems. The response system is considered an important component of IDS, since without a timely response IDSs may not function properly in countering various attacks, especially on a real-time basis. To respond appropriately, IDSs should select the optimal response option according to the type of network attack. This research study provides a complete survey of IDSs and Intrusion Response Systems (IRSs) on the basis of our in-depth understanding of the response option for different types of network attacks. Knowledge of the path from IDS to IRS can assist network administrators and network staffs in understanding how to tackle different attacks with state-of-the-art technologies.