Privacy Regulations Research Articles

Grading by AI makes me feel fairer? How different evaluators affect college students' perception of fairness.

In the field of education, new technologies have enhanced the objectivity and scientificity of educational evaluation. However, concerns have been raised about the fairness of evaluators, such as artificial intelligence (AI) algorithms. This study aimed to assess college students' perceptions of fairness in educational evaluation scenarios through three studies using experimental vignettes. Three studies were conducted involving 172 participants in Study 1, 149 in Study 2, and 145 in Study 3. Different evaluation contexts were used in each study to assess the influence of evaluators on students' perception of fairness. Information transparency and explanations for evaluation outcomes were also examined as potential moderators. Study 1 found that different evaluators could significantly influence the perception of fairness under three evaluation contexts. Students perceived AI algorithms as fairer evaluators than teachers. Study 2 revealed that information transparency was a mediator, indicating that students perceived higher fairness with AI algorithms due to increased transparency compared with teachers. Study 3 revealed that the explanation of evaluation outcomes moderated the effect of evaluator on students' perception of fairness. Specifically, when provided with explanations for evaluation results, the effect of evaluator on students' perception of fairness was lessened. This study emphasizes the importance of information transparency and comprehensive explanations in the evaluation process, which is more crucial than solely focusing on the type of evaluators. It also draws attention to potential risks like algorithmic hegemony and advocates for ethical considerations, including privacy regulations, in integrating new technologies into educational evaluation systems. Overall, this study provides valuable theoretical insights and practical guidance for conducting fairer educational evaluations in the era of new technologies.

Challenges in implementing free software in small and medium-sized enterprises in the city of Montería: a case study

This study investigates challenges and opportunities in adopting free and open-source software (FOSS) in small and medium-sized enterprises (SMEs) in Monteria, Colombia. The research reveals that around 77.5% of SMEs prefer free software, yet surprisingly, 80% are unaware of the benefits of open-source licenses, with nearly 45% not adopting them due to lack of knowledge. Implementing FOSS in SMEs offers legal and economic advantages, including reduced software acquisition costs, compliance with data protection and privacy regulations, and fostering innovation. However, adoption barriers persist, necessitating further research for enhancing implementation in Colombian SMEs. Notably, Colombia's ethical framework for AI serves as a guide for ethical AI and open-source software deployment, aligned with sustainable development goals. This study highlights free software usage prevalence in Monteria's SMEs and critical factors hindering full adoption. Addressing challenges and leveraging potential benefits can improve efficiency, regulatory compliance, and contribute to sustainable development. Continued research in this field can promote broader and stronger implementation of FOSS in Colombian SMEs.

A comprehensive examination of security and privacy in precision agriculture technologies

Precision agriculture has revolutionized modern farming practices by integrating cutting-edge technologies such as sensors, drones, and data analytics to optimize crop management. While these advancements offer unprecedented benefits in terms of yield optimization and resource efficiency, they also raise significant concerns regarding the security and privacy of sensitive agricultural data. This research paper delves into the intricate landscape of precision agriculture security and privacy, aiming to identify potential threats, vulnerabilities, and the corresponding measures necessary for safeguarding farmers' data and ensuring the sustainability of precision farming practices. The study employs a multidisciplinary approach, combining insights from computer science, agriculture, and privacy studies to provide a comprehensive overview. Through an analysis of existing security frameworks and privacy regulations, this paper proposes recommendations for the development and implementation of robust security protocols and privacy-enhancing technologies tailored to the unique challenges of precision agriculture. The findings of this research contribute to the ongoing dialogue on responsible technology adoption in agriculture, emphasizing the importance of striking a balance between innovation and the protection of farmers' sensitive information in the era of precision farming.

IoT Complexity: Security, Vulnerabilities and Risks

IoT privacy highlights the critical significance of tailored threat models to address the evolving challenges in the IoT landscape. This research paper presents an in-depth examination of privacy threat modeling in the context of the Internet of Things (IoT), and aims to develop threat models tailored to different IoT device categories, identifying vulnerabilities and potential privacy risks associated with each. This analysis seeks to provide insights into the diverse privacy challenges posed by IoT devices, ranging from wearables to healthcare IoT and smart home, which exhibit unique vulnerabilities and privacy risks. By developing threat models specific to each device category, this research elucidates the diversity of privacy concerns, such as data breaches, unauthorized access, and data tracking the applicability of privacy regulations varies across these categories, emphasizing the need for tailored regulatory frameworks. The research underscores the importance of user education and responsible device design, advocating for privacy literacy and transparency, as it ensures that privacy is an integral part of the development process, fostering a safer, more secure, and privacy-conscious IoT ecosystem where innovation and privacy coexist for the benefit of all.

Shedding Light on the Inadequate Protection of Internet Users’ Right to Privacy under Kuwaiti Law

Abstract Digital technology such as AI has enabled private companies to collect and analyse people’s personal information, which may infer hidden private data about the individual, such as medical data or psychological state. The inferred private information can be sold to third parties or used to target advertisements without the individual’s knowledge and explicit consent. Private companies, therefore, pose a risk to internet users’ right to privacy in the digital world. As such, the legal system should have a comprehensive Data Privacy law that provides internet users with adequate legal protection for preserving their right to privacy against private companies. Still, Kuwait lacks comprehensive Data Privacy. The only two data privacy mechanisms are embodied in chapter 7 under the Electronic Transaction Act (ETA) No. 20 of 2014 and the Regulation of User Protection and Privacy issued by the Communication and Information Technology Regulatory Authority (CITRA). This paper finds that the fragmented Kuwaiti laws concerning the individual’s right to privacy inadequately protect internet users’ right to privacy against the risk posed by private companies, and therefore, legislative interventions are needed. This paper concludes with proposing essential recommendations for the Kuwaiti Legislative Authority to issue a comprehensive data privacy law, using European Union’s General Data Protection Regulation as a guidance framework (GDPR), to strengthen internet users’ right to privacy (as control over their personal information) against the risk private companies pose.

Consumer Data Without Compromise: Integrating Differential Privacy and GANs for Privacy-Preserving Digital Marketing

In the rapidly evolving digital marketing landscape, the utilization of consumer data is essential for efficient targeting and personalization of marketing practices. However, the growing concerns regarding user privacy and stringent data protection regulations have created challenges in accessing and using consumer data for marketing purposes. This paper introduces a novel approach that leverages Differential Privacy and Conditional Tabular Generative Adversarial Networks (CTGAN) to address these privacy concerns while maintaining the efficacy of data-driven digital marketing strategies. Our approach amalgamates the strengths of Differential Privacy and CTGAN, applying differential privacy to the original dataset to ensure that extracted data cannot be tied back to individuals. We then train a CTGAN on an open marketing dataset to learn and generate synthetic data of close resemblance. Through extensive empirical analysis, we evaluate the fidelity, utility, and trade-offs of our approach, demonstrating its effectiveness in synthesizing non-Gaussian and multi-modal distributions, and its applicability in real-world classification problems. The research also highlights the complexity of hyperparameter tuning and the importance of a balanced approach in model training. Our findings contribute valuable insights to both the theoretical understanding of generative models and practical guidance for digital marketing practitioners

GDPR and the indefinable effectiveness of privacy regulators: Can performance assessment be improved?

Abstract Data protection regulations like the General Data Protection Regulation (GDPR) are increasingly important in securing individuals’ privacy as society goes digital. The success of any regulation, however good, ultimately depends on how well it is executed. Existing literature fails to answer what good execution means in this context. We research what practitioners think are the objectives of data protection regulators and how they evaluate their effectiveness. We explore novel ways to assess regulator performance more systematically. We surveyed 70 chief information security officers and conducted 23 structured interviews. The interviewees included informed business executives, lawyers, digital rights activists, and four national regulators. We supplement it with an analysis of diverse enforcement databases. Our findings indicate a mismatch between the broad presumed objectives attributed to regulators and the narrow criteria used to judge them in practice. Perception of the regulator’s effectiveness is subjective, sanctions-focused, and influenced by one’s role and responsibilities. Moreover, the independence of regulators, intentionally designed to insulate them from daily politics, raises serious questions of accountability. We examine the historical, cultural, and organizational motivations behind the current byzantine complexity of the GDPR regime. Lastly, we contribute a series of key performance indicators and make structural suggestions around centralized and standardized reporting of cases to deliver improved learning, legitimacy, transparency, and comparability. We believe our findings have important implications for the future development of regulator assessment and accountability in Europe and in the growing number of GDPR-like regimes outside Europe.

Automated Analysis of Changes in Privacy Policies: A Structured Self-Attentive Sentence Embedding Approach

The increasing societal concern for consumer information privacy has led to the enforcement of privacy regulations worldwide. In an effort to adhere to privacy regulations such as General Data Protection Regulation (GDPR), many companies’ privacy policies have become increasingly lengthy and complex. In this study, we adopted the computational design science paradigm to design a novel privacy policy evolution analytics framework to help identify how companies change and present their privacy policies based on privacy regulations. The framework includes a Self-Attentive Annotation System (SAAS) that automatically annotates paragraph-length segments in privacy policies to help stakeholders identify data practices of interest for further investigation. We rigorously evaluated SAAS against state-of-the-art Machine Learning (ML) and Deep Learning (DL)-based methods on a well-established privacy policy dataset, OPP-115. SAAS outperformed conventional ML and DL models in terms of F1-score by statistically significant margins. We demonstrate the proposed framework’s practical utility with an in-depth case study of GDPR’s impact on Amazon’s privacy policies. The case study results indicate that Amazon’s post-GDPR privacy policy potentially violates a fundamental principle of GDPR by causing consumers to exert more effort to find information about first-party data collection. Given the increasing importance of consumer information privacy, the proposed framework has important implications for regulators and companies. We discuss several design principles followed by the SAAS that can help guide future design science-based e-commerce, health, and privacy research.

Federated Meta Embedding Concept Stock Recommendation

Mining relevant stocks given a trending topic/concept in capital markets is an application with significant economic and societal impacts. Previous concept stock recommendation system mines concept stocks only from public social media like financial news. On stock forums, investors discuss emerging concepts and stocks by using forum comments, which are unneglectable resources to capture trending concept stocks accurately and timely. However, the comment data from a single forum is insufficient to build a high-quality recommendation system. The forums are data silos protected by privacy regulations, and their comments are still underutilized. In this paper, we propose a federated concept stock recommendation baseline and an optimized method that both leverage the private forum comments and public social media without compromising privacy regulations. Our baseline, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , Federated Meta Embedding (FedME), is built upon the federated learning framework and learns a concept-stock embedding jointly from private and public data. Our optimized method, Federated Graph Meta Embedding (FedGME), improves FedME by using a graph to combine two sources of embeddings and additional human experts' concept-stock knowledge. Empirically, the experiments on two concept stock datasets show that FedME and FedGME substantially improve the performance of recommendation. Our methods provide practical guidance on privacy-preserving FinTech applications.

Privacy Regulation and Targeted Advertising: Evidence from Apple's App Tracking Transparency

Privacy Regulation and Targeted Advertising: Evidence from Apple's App Tracking Transparency

Data Privacy in the Digital Age: Analyzing the impact of Technology of U.S Privacy Regulations

Data Privacy in the Digital Age: Analyzing the impact of Technology of U.S Privacy Regulations

Privacy-preserving authentication and authorization in networks using blockchain

Blockchain-based solutions offer a promising avenue for privacy-preserving authentication and authorization mechanisms. Through the immutable and decentralized nature of blockchain, individuals can maintain control over their personal data while still engaging in secure transactions and interactions. These solutions leverage cryptographic techniques to ensure privacy, such as zero-knowledge proofs, which allow one party to prove possession of certain information without revealing the information itself.By storing authentication and authorization data on the blockchain, users can access services without having to repeatedly provide sensitive information. Smart contracts can automate authorization processes, ensuring that only authorized parties can access certain resources or perform specific actions. Additionally, blockchain-based identity systems offer a self-sovereign approach, where individuals have full control over their digital identities, reducing the reliance on centralized authorities. Moreover, blockchain networks provide transparency and auditability, allowing users to track how their data is being used and ensuring compliance with privacy regulations. However, challenges such as scalability, interoperability, and user adoption remain to be addressed for widespread implementation. Overall, blockchain-based solutions hold great potential in providing privacy-preserving authentication and authorization while empowering individuals with greater control over their data.Blockchain-based solutions offer a promising avenue for privacy-preserving authentication and authorization mechanisms. Through the immutable and decentralized nature of blockchain, individuals can maintain control over their personal data while still engaging in secure transactions and interactions. These solutions leverage cryptographic techniques to ensure privacy, such as zero-knowledge proofs, which allow one party to prove possession of certain information without revealing the information itself.

Enhancing facial feature de-identification in multiframe brain images: A generative adversarial network approach.

The collection of head images for public datasets in the field of brain science has grown remarkably in recent years, underscoring the need for robust de-identification methods to adhere with privacy regulations. This paper elucidates a novel deep learning-based approach to deidentifying facial features in brain images using a generative adversarial network to synthesize new facial features and contours. We employed the precision of the three-dimensional U-Net model to detect specific features such as the ears, nose, mouth, and eyes. Results: Our method diverges from prior studies by highlighting partial regions of the head image rather than comprehensive full-head images. We trained and tested our model on a dataset comprising 490 cases from a publicly available head computed tomography image dataset and an additional 70 cases with head MR images. Integrated data proved advantageous, with promising results. The nose, mouth, and eye detection achieved 100% accuracy, while ear detection reached 85.03% in the training dataset. In the testing dataset, ear detection accuracy was 65.98%, and the validation dataset ear detection attained 100%. Analysis of pixel value histograms demonstrated varying degrees of similarity, as measured by the Structural Similarity Index (SSIM), between raw and generated features across different facial features. The proposed methodology, tailored for partial head image processing, is well suited for real-world imaging examination scenarios and holds potential for future clinical applications contributing to the advancement of research in de-identification technologies, thus fortifying privacy safeguards.

Securing the Future of Digital Marketing through Advanced Cybersecurity Approaches and Consumer Data Protection Privacy and Regulatory Compliance

The SecureDigitalGuard framework gets recognition for its all-encompassing strategy, which combines strict consumer data protection laws with state-of-the-art security safeguards with ease. This all-encompassing approach is designed to guarantee the longevity of digital marketing in the face of constantly changing cyberthreats. This cutting-edge system is based on three key strategies: the Behavioural Threat Detection (BTD) algorithm, the Adaptive Access Control (AAC) algorithm, and the Homomorphic Privacy Guard (HPG) programme. The vital task of dynamically controlling user access levels in response to continuing risk evaluations is taken on by the AAC algorithm. This dynamic control technique improves the framework's capacity to adjust to constantly shifting security circumstances. However, the BTD algorithm is proactive in spotting abnormalities in user behaviour, allowing for quick reactions to any dangers. The SecureDigitalGuard architecture gains an additional degree of protection from this preventive method. In addition, the HPG programme is responsible for doing analytics while maintaining user privacy. This careful approach shows a dedication to finding a fine balance between user protection and data analysis, making sure the framework complies with the strictest privacy regulations. Test results provide empirical evidence that SecureDigitalGuard is effective and that it can keep up with the dynamic and often changing nature of cyber threats. As a result, the architecture makes traditional cybersecurity techniques outdated. In an increasingly complex and dynamic cybersecurity world, SecureDigitalGuard provides a strong solution for protecting digital marketing through the seamless integration of state-of-the-art technology and strict adherence to privacy regulations.

Designing Effective Privacy Notices and Controls

Privacy notice and choice are essential aspects of privacy and data protection regulation worldwide. Yet, today's privacy notices and controls are surprisingly ineffective at informing users or allowing them to express choice. We analyze why existing privacy notices fail to inform users and tend to leave them helpless, and discuss principles for designing more effective privacy notices and controls.

A Comparison Between Enforcement of the European Union General Data Protection Regulation (GDPR) and the United States Privacy Regulations

A Comparison Between Enforcement of the European Union General Data Protection Regulation (GDPR) and the United States Privacy Regulations

Cross-Channel Attribution Modeling in the Age of Privacy Regulations

Cross-Channel Attribution Modeling in the Age of Privacy Regulations

Optimal Privacy Regulation When Consumers Make Inferences from Regulatory Policy

Optimal Privacy Regulation When Consumers Make Inferences from Regulatory Policy

The Intended and Unintended Consequences of Privacy Regulation for Consumer Marketing: A Marketing Science Institute Report

The Intended and Unintended Consequences of Privacy Regulation for Consumer Marketing: A Marketing Science Institute Report

Security and Privacy in AI-Driven Industry 5.0: Experimental Insights and Threat Analysis

This empirical research offers important insights from simulated industrial situations as it examines security and privacy in AI-driven Industry 5.0. When responding to security problems, participants' remarkable average reaction time of 14 minutes demonstrated their preparedness. On a 5-point rating scale, the clarity and openness of privacy rules were scored 3.8 overall; however, differences between 3.5 and 4.2 indicated the range of privacy issues. These results highlight the need of well-defined security procedures, thorough training, and easily available, transparent privacy regulations in order to manage the ethical integration of AI into Industry 5.0 and promote stakeholder confidence and data protection.