Applications of Difital Image Stegnographic Techniques in Medical Image Analysis

In this digitized world maintaining the security of the secret information is a challenging task. While, sending secret information through the internet draws the attention of hackers.  The highly authenticated information can be hidden by using Steganography. Image processing plays a very important role in such stenographic techniques. The advantage of stenography can be enhanced to medical images and creation of database for a particular patient under one authentication with security. Steganography techniques used in bio-medical field to hide the person medical data like prescription, X-ray, Iris, MRI, CT scan images behind a single cover media. In this paper the embedding Schemes to store complete medical data under one authentication is done by using Spatial and transform domains. The performance of the techniques is compared and the best method to hide medical information by using steganographic techniques with high PSNR, less MSE and high SSIM is identified for different modalities. Implementation of steganography in bio-medical field yields high imperceptibility and embedding capacit

State-of-the-art Survey of Data Hiding in ECG Signal

With the development of new communication technologies, the number of biomedical data that is transmitted is constantly increasing. This is sensitive data and therefore it is very important to preserve privacy when transmitting it. For this purpose, techniques for data hiding in biomedical signals are used. This is a comprehensive survey of research papers that covers the latest techniques for data hiding in ECG signal and old techniques that are not covered by the latest surveys. We show an overview of the methodology, robustness, and imperceptibility of the techniques

A Universal Cybersecurity Competency Framework for Organizational Users

The global reliance on the Internet to facilitate organizational operations necessitates further investments in organizational information security. Such investments hold the potential for protecting information assets from cybercriminals. To assist organizations with their information security, The National Initiative for Cybersecurity Education (NICE) Cybersecurity Workforce Framework (NCWF) was created. The framework referenced the cybersecurity work, knowledge, and skills required to competently complete the tasks that strengthen their information security. Organizational users’ limited cybersecurity competency contributes to the financial and information losses suffered by organizations year after year. While most organizational users may be able to respond positively to a cybersecurity threat, without a measure of their cybersecurity competency they represent a cybersecurity threat to organizations. The main goal of this research study was to develop a universal Cybersecurity Competency Framework (CCF) to determine the demonstrated cybersecurity Knowledge, Skills, and Tasks (KSTs) through the NCWF (NICE, 2017) as well as identify the cybersecurity competency of organizational users. Limited attention has been given in cybersecurity research to determine organizational users’ cybersecurity competency. An expert panel of cybersecurity professionals known as Subject Matter Experts (SMEs) validated the cybersecurity KSTs necessary for the universal CCF. The research study utilized the explanatory sequential mixed-method approach to develop the universal CCF. This research study included a developmental approach combining quantitative and qualitative data collection in three research phases. In Phase 1, 42 SMEs identified the KSTs needed for the universal CCF. The results of the validated data from Phase 1 were inputted to construct the Phase 2 semi-structured interview. In Phase 2, qualitative data were gathered from 12 SMEs. The integration of the quantitative and qualitative data validated the KSTs. In Phase 3, 20 SMEs validated the KST weights and identified the threshold level. Phase 3 concluded with the SMEs\u27 aggregation of the KST weights into the universal CCF index. The weights assigned by the SMEs in Phase 3 showed that they considered knowledge as the most important competency, followed by Skills, then Tasks. The qualitative results revealed that training is needed for cybersecurity tasks. Phase 3 data collection and analysis continued with the aggregation of the validated weights into a single universal CCF index score. The SMEs determined that 72% was the threshold level. The findings of this research study significantly contribute to the body of knowledge on information systems and have implications for practitioners and academic researchers. It appears this is the only research study to develop a universal CCF to assess the organizational user’s competency and create a threshold level. The findings also offer further insights into what organizations need to provide cybersecurity training to their organizational users to enable them to competently mitigate cyber-attacks

Preserving privacy in edge computing

Edge computing or fog computing enables realtime services to smart application users by storing data and services at the edge of the networks. Edge devices in the edge computing handle data storage and service provisioning. Therefore, edge computing has become a  new norm for several delay-sensitive smart applications such as automated vehicles, ambient-assisted living, emergency response services, precision agriculture, and smart electricity grids. Despite having great potential, privacy threats are the main barriers to the success of edge computing. Attackers can leak private or sensitive information of data owners and modify service-related data for hampering service provisioning in edge computing-based smart applications. This research takes privacy issues of heterogeneous smart application data into account that are stored in edge data centers. From there, this study focuses on the development of privacy-preserving models for user-generated smart application data in edge computing and edge service-related data, such as Quality-of-Service (QoS) data, for ensuring unbiased service provisioning. We begin with developing privacy-preserving techniques for user data generated by smart applications using steganography that is one of the data hiding techniques. In steganography, user sensitive information is hidden within nonsensitive information of data before outsourcing smart application data, and stego data are produced for storing in the edge data center. A steganography approach must be reversible or lossless to be useful in privacy-preserving techniques. In this research, we focus on numerical (sensor data) and textual (DNA sequence and text) data steganography. Existing steganography approaches for numerical data are irreversible. Hence, we introduce a lossless or reversible numerical data steganography approach using Error Correcting Codes (ECC). Modern lossless steganography approaches for text data steganography are mainly application-specific and lacks imperceptibility, and DNA steganography requires reference DNA sequence for the reconstruction of the original DNA sequence. Therefore, we present the first blind and lossless DNA sequence steganography approach based on the nucleotide substitution method in this study. In addition, a text steganography method is proposed that using invisible character and compression based encoding for ensuring reversibility and higher imperceptibility.  Different experiments are conducted to demonstrate the justification of our proposed methods in these studies. The searching capability of the stored stego data is challenged in the edge data center without disclosing sensitive information. We present a privacy-preserving search framework for stego data on the edge data center that includes two methods. In the first method, we present a keyword-based privacy-preserving search method that allows a user to send a search query as a hash string. However, this method does not support the range query. Therefore, we develop a range search method on stego data using an order-preserving encryption (OPE) scheme. In both cases, the search service provider retrieves corresponding stego data without revealing any sensitive information. Several experiments are conducted for evaluating the performance of the framework. Finally, we present a privacy-preserving service computation framework using Fully Homomorphic Encryption (FHE) based cryptosystem for ensuring the service provider's privacy during service selection and composition. Our contributions are two folds. First, we introduce a privacy-preserving service selection model based on encrypted Quality-of-Service (QoS) values of edge services for ensuring privacy. QoS values are encrypted using FHE. A distributed computation model for service selection using MapReduce is designed for improving efficiency. Second, we develop a composition model for edge services based on the functional relationship among edge services for optimizing the service selection process. Various experiments are performed in both centralized and distributed computing environments to evaluate the performance of the proposed framework using a synthetic QoS dataset