The widespread adoption of digital technologies in tourism has transformed the data privacy landscape, necessitating stronger safeguards. This study examines the evolving research environment of digital privacy in tourism management, focusing on publication trends, collaborative networks, and social contract theory. A mixed-methods approach was employed, combining bibliometric analysis, social contract theory, and qualitative content analysis. Data from 2004 to 2023 were analyzed using network visualization tools to identify key researchers and trends. The study highlights a significant increase in academic attention after 2015, reflecting the industry's growing recognition of digital privacy as crucial. Social contract theory provided a framework emphasizing transparency, consent, and accountability. The study also examined high-impact articles and the role of publishers like Elsevier and Wiley. The findings offer practical insights for policymakers, industry leaders, and researchers, advocating for ongoing collaboration to address privacy challenges in tourism.

The rapid expansion of smart cities has led to the widespread deployment of Internet of Things (IoT) devices for real-time data collection and urban optimization. However, these interconnected systems face critical cybersecurity risks, including data tampering, unauthorized access, and privacy breaches. This paper proposes a blockchain-based framework designed to enhance the security, integrity, and resilience of IoT data in smart city environments. Leveraging a private blockchain, the system ensures decentralized, tamper-proof data storage, and transaction verification through digital signatures and a lightweight Proof of Work consensus mechanism. Smart contracts are employed to automate access control and respond to anomalies in real time. A Python-based simulation demonstrates the framework’s effectiveness in securing IoT communications. The system supports rapid transaction validation with minimal latency and enables timely detection of anomalous patterns through integrated machine learning. Evaluations show that the framework maintains consistent performance across diverse smart city components such as transportation, healthcare, and building security. These results highlight the potential of the proposed solution to enable secure, scalable, and real-time IoT ecosystems for modern urban infrastructures.

Divorce for female civil servants in Indonesia is more complex than for non-civil servants due to a pseudo-administrative process. This condition requires submitting a written application for divorce permission to their agency and proceeding through multiple lengthy stages. During this process, women must verbally disclose sensitive personal details to state authorities. Failure to obtain written permission or to report the divorce within a specific period can result in disciplinary action. This paper examines how female civil servants protect their privacy while seeking divorce permission, focusing on managing personal information, controlling divorce-related details at work, and handling the information turbulence that arises. The researcher collected data from 12 female civil servants at Indonesia’s Directorate General of Taxes (DGT) who had applied for divorce permission. The findings reveal the subjective experiences and strategies women civil servants use to manage sensitive personal issues. The quasi-administrative nature of the divorce permit process introduces complexities that extend beyond formal procedures. Regulations governing the submission of divorce permits, overseen by government agencies, often add to the burden these women face, neglecting their privacy and psychological well-being. Impartial individuals and gender preferences in the verification team can exacerbate distress. Therefore, revising the divorce permit regulations to enhance privacy and sensitivity is crucial. The study recommends early information about the process and communication training for maintaining privacy.

The usage of cybersecurity is growing steadily because it is beneficial to us. When people use cybersecurity, they can easily protect their valuable data. Today, everyone is connected through the internet. It’s much easier for a thief to connect important data through cyber-attacks. Everyone needs cybersecurity to protect their precious personal data and sustainable infrastructure development in data science. However, systems protecting our data using the existing cybersecurity systems is difficult. There are different types of cybersecurity threats. It can be phishing, malware, ransomware, and so on. To prevent these attacks, people need advanced cybersecurity systems. Many software helps to prevent cyber-attacks. However, these are not able to early detect suspicious internet threat exchanges. This research used machine learning models in cybersecurity to enhance threat detection. Reducing cyberattacks internet and enhancing data protection; this system makes it possible to browse anywhere through the internet securely. The Kaggle dataset was collected to build technology to detect untrustworthy online threat exchanges early. To obtain better results and accuracy, a few pre-processing approaches were applied. Feature engineering is applied to the dataset to improve the quality of data. Ultimately, the random forest, gradient boosting, XGBoost, and Light GBM were used to achieve our goal. Random forest obtained 96% accuracy, which is the best and helpful to get a good outcome for the social development in the cybersecurity system.

This study aims to use dialectical thinking to explore the impacts and responses of Artificial Intelligence (AI) empowerment on students’ personalized learning. The effect of AI empowerment on student personalization is dissected through a literature review and empirical cases. The study finds that AI plays a significant role in promoting personalized learning by enhancing students’ learning effectiveness through intelligent recommendation, automated feedback, improving students’ independent learning ability, and optimizing learning paths, however, the wide application of AI also brings problems such as technological dependence, cheating in exams, weakening of critical thinking ability, educational fairness, and data privacy protection to students. The study proposes recommendations to strengthen technology regulation, enhance the synergy between teachers and AI, and optimize the personalized learning model. AI-enabled personalized learning is expected to play a greater role in improving learning efficiency and educational fairness.

The privacy of personal information is aimed at protecting human rights both under the international human rights regime and the Saudi Arabian constitution and other statutes and regulations, subject only to some exceptions that include the protection of public health. The coronavirus disease 2019 (COVID-19) pandemic has brought about certain challenges that necessitate strategies to augment the conventional surveillance of infectious diseases, contact tracing, isolation, reporting and vaccination. Several governments institutions, and agencies presently adopt mobile applications for collecting, analyzing, managing, and sharing critical personal data of individuals infected with or exposed to COVID-19. While the benefits of sharing private information for achieving public health needs may not be disputed, the risk of breach of personal privacy is enormous. This had forced the national governments into a dilemma of either succumbing to public health needs, strictly respecting and protecting the privacy of individuals, or alternatively, balancing the two conflicting demands. There is a massive body of literature on the security and privacy of such mobile applications, but none has adequately explored and discussed public interest justifications under Saudi Arabian laws for alleged privacy breaches. We examined the health surveillance mobile app technologies currently in use in Saudi Arabia with the aim of determining the potential risks of data breaches under extant data protection laws. The paper recommends, among others, that any potential risk of breach to right to privacy of personal information under the law must be (justified by) the public health needs to protect society during the COVID-19 pandemic.