Implementing Privacy by Design through Privacy Impact Assessments

Privacy has come a long way from being a fundamental physical right to being implemented as virtual online privacy under GDPR. Recent privacy breaches around the world have highlighted the role of the design of information systems in protecting the privacy of individuals online. GDPR envisions to achieve this through Privacy by Design (PbD) in business and technological systems. Privacy by Design is the law regulating the architecture of information systems through its code and organizational measures to facilitate usercentric privacy. It is relatively a new concept initially developed by Ann Cavoukian along with PbD Principles. The principles themselves do not ensure the holistic implementation of the PbD process. What is lacking in the current model of PbD is an implementation mechanism to operationalize the PbD as a process. This study builds upon the model suggested by Kroener and Wright to operationalize PbD through a dual approach: a set of principles (PbD Principles) and a process (PIAs). Firstly, this study starts an informed discussion on PbD and its robust theoretical basis under Lessig's Theory of Regulation. Secondly, it proposes to address the lack of operationalization by using Privacy Impact Assessments (PIAs) as a tool to conduct the PbD process. It brings together the two concepts and shows how PbD, as a process, can be better performed if complemented with PIAs. Lastly, it develops a framework for such a PbD process and constructs a lifecycle model to address the gaps in its operationalization. It demonstrates the feasibility of the developed PbD operationalization model by applying it to an existing information system: the Föli Mobile Application

Next-generation cyber attack prediction for IoT systems: leveraging multi-class SVM and optimized CHAID decision tree

Abstract Billions of gadgets are already online, making the IoT an essential aspect of daily life. However, the interconnected nature of IoT devices also leaves them open to cyber threats. The quantity and sophistication of cyber assaults aimed against Internet of Things (IoT) systems have skyrocketed in recent years. This paper proposes a next-generation cyber attack prediction framework for IoT systems. The framework uses the multi-class support vector machine (SVM) and the improved CHAID decision tree machine learning methods. IoT traffic is classified using a multi-class support vector machine to identify various types of attacks. The SVM model is then optimized with the help of the CHAID decision tree, which prioritizes the attributes most relevant to the categorization of attacks. The proposed framework was evaluated on a real-world dataset of IoT traffic. The findings demonstrate the framework's ability to categorize attacks accurately. The framework may determine which attributes are most crucial for attack categorization to enhance the SVM model's precision. The proposed technique focuses on network traffic characteristics that can be signs of cybersecurity threats on IoT networks and affected Network nodes. Selected feature vectors were also created utilizing the elements acquired on every IoT console. The evaluation results on the Multistep Cyber-Attack Dataset (MSCAD) show that the proposed CHAID decision tree can significantly predict the multi-stage cyber attack with 99.72% accuracy. Such accurate prediction is essential in managing cyber attacks in real-time communication. Because of its efficiency and scalability, the model may be used to forecast cyber attacks in real time, even in massive IoT installations. Because of its computing efficiency, it can make accurate predictions rapidly, allowing for prompt detection and action. By locating possible entry points for attacks and mitigating them, the framework helps strengthen the safety of IoT systems

Improving carvacrol bioaccessibility using core–shell carrier-systems under simulated gastrointestinal digestion

The impact of encapsulating carvacrol in chitosan-albumin based core–shell nano-carriers (NCs) on its stability and bioaccessibility was determined under simulated digestion conditions. These NCs consisted of chitosan (C) core enclosed by bovine serum albumin (BSA) shell. The mean particle size ranged from 52.4 ± 10 nm to 203 ± 6 nm and zeta-potential from + 21 ± 3.6 to −18 ± 2.7 mV. The size and charge were significantly modified after the protein-shell formation around the polysaccharide-core. Core-shell NCs were more stable, with less aggregation under simulated gastrointestinal conditions than C-NCs, presumably due to greater steric repulsion. Likewise, core–shell NCs were observed relatively more stabilized in the intestinal phase than gastric phase. The bioaccessibility of carvacrol was enhanced significantly when it was encapsulated in the core–shell NCs. These findings imply that C-BSA based core–shell NCs might be an efficient means of encapsulating, protecting and delivering hydrophobic bioactive compounds for applications in functional foods