Previous studies suggest that social media use among the youth is correlated with online and offline political participation. There is also a mixed and inconclusive debate on whether online political participation in the youth increases offline political participation. This study uses three models of OLS, two-way fixed effects, and an instrumental variable approach to make causal inferences about the social media use and online and offline political participation of the youth. The analyses provide evidence of a significant effect of casual social media use on online political participation and no effect or negligible effect on offline political activity and voting behavior. The results from fixed effects and instrumental variable models provide strong evidence of elasticity between online political participation and offline political activity in young individuals. On average, a one percent increase in online political participation increases the offline political activity index by 0.12 percent.

This study presents a narrative review of the literature on privacy concerns of Open Government Data (OGD) programs and identifies suggested technical, procedural, and legal remedies. Peer-reviewed articles were identified and analysed from major bibliographic databases, including Web of Science, Digital ACM Library, IEEE Explore Digital Library, and Science Direct. Included articles focus on identifying individual information privacy concerns from the viewpoint of OGD stakeholders or providing solutions for mitigating concerns and risks. Papers that discussed and focused on general privacy issues or privacy concerns of open data in general or open science privacy concerns were excluded. Three streams of research were identified: 1) exploring privacy concerns and balance with OGD value propositions, 2) proposing solutions for mitigating privacy concerns, and 3) developing risk-based frameworks for the OGD program at different governmental levels. Findings suggest that contradictions with Fair Information Practices, reidentification risks, conflicts with OGD value propositions, and smart city data practices are significant privacy concerns in the literature. Proposed solutions include technical, legal, and procedural measures to mitigate privacy concerns. Building on the findings, practical implications and suggested future research directions are provided.

Electronic health record (EHR) data breaches create severe concerns for patients’ privacy, safety, and risk of loss for healthcare entities responsible for managing patient health records. EHR systems collect a vast amount of user-sensitive data, requiring integration, implementation, and the application of essential security principles, controls, and strategies to safeguard against persistent adversary attacks. This research is an exploratory study into current integrated EHR cybersecurity attacks using United States Health Insurance Portability and Accountability Act (HIPAA) privacy and security breach reported data. This work investigates if current EHR implementation lacks the requisite security control to prevent a cyber breach and protect user privacy. We conduct descriptive and trend analysis to describe, demonstrate, summarize data points, and predict direction based on current and historical data by covered entity, type of breaches, and point of breaches (examine, attack methods, patterns, and location of breach information). An Autoregressive Integrated Moving Average (ARIMA) model is used to provide a detailed analysis of the data demonstrating breaches caused by hacking and IT incidents show a significant trend (coefficient 0.84, p-value < 2.2 × 10⁻¹⁶ ***). The findings reveal a consistent rise in breaches—particularly from hacking and IT incidents—disproportionately affecting healthcare providers. The study highlights that EHR data breaches often follow recurring patterns, indicating common vulnerabilities, and underlines the need for prioritized, data-driven security investments. These findings validate the hypothesis that most EHR cybersecurity attacks are concentrated using similar attack methodologies and face common vulnerabilities and demonstrate the value of targeted mitigation strategies to strengthen healthcare cybersecurity. The findings highlight the urgent need for healthcare organizations and policymakers to prioritize targeted, data-driven security investments and enforce stricter controls to protect EHR systems from increasingly frequent and predictable cyberattacks.

Higher dimensionality of data that has to be analyzed for detecting attack is one of the key issues concerning intrusion detection system (IDS). This is due to the different features in the attack on network that eats up time in terms of training and prediction setups. In this study a hybrid method of Support Vector Machine (SVM) as well as Genetic Algorithm (GA) is suggested and their implementation in IDS is outlined. The suggested methods are used for reducing the number of features from 41 to 11 using KDD Cup’99 dataset. The features are classified as three priorities using GA with the most significant as the first priority and the least one as the third priority. The way in which feature distribution is done is that four features are placed in the first priority, five in the second and two in the third. The results show that the suggested hybrid algorithms, GA and SVM are able to achieve true and false positive values of 0.973 and 0.017 respectively. Copyright © 2015 Praise Worthy Prize - All rights reserved.

Electronic Health Record (EHR) data breaches create severe concerns for patients' privacy, safety, and risk of loss for healthcare entities responsible for managing patient health records. EHR systems collect a vast amount of user-sensitive data, requiring integration, implementation, and application of essential security principles, controls, and strategies to safeguard against persistent adversary attacks. This research is an exploratory study into current Artificial Intelligence of Things integrated EHR cybersecurity attacks using United States Health Insurance Portability and Accountability Act (HIPAA) privacy and security breach reported data. A descriptive and trend analysis is conducted to describe, demonstrate, summarize data points, and predict direction based on current and historical data by covered entity, type of breaches, and point of breaches (examine, attack methods, patterns, and location of breach information). ARIMA model is used to provide a detailed analysis of the data demonstrating breaches caused by hacking and IT incidents show a significant trend (coefficient 0.84, p-value < 2.2e-16 ***). The finding shows individual records in breach incidents on all categories of covered entities are skewed toward zero, demonstrating that healthcare providers are at the top in the number of breaches consistently during the analyzed period, and the trend is increasing with a number of breach incidents attributed to “Hacking/IT” has been increasing consistently throughout 2010 to 2022. The analysis validated that Artificial Intelligence of Things integrated EHR implementation lacks sufficient security controls to guarantee patient privacy, safety, and hospital operation continuity during a cyberattack.