Privacy Framework Research Articles

A Randomized Response Framework to Achieve Differential Privacy in Medical Data

In recent years, differential privacy has gained substantial traction in the medical domain, where the need to balance privacy preservation with data utility is paramount. As medical data increasingly relies on cloud platforms and distributed sharing among multiple stakeholders, such as healthcare providers, researchers, and policymakers, the importance of privacy-preserving techniques has become more pronounced. Trends in the field focus on designing efficient algorithms tailored to high-dimensional medical datasets, incorporating privacy guarantees into federated learning for distributed medical devices, and addressing challenges posed by adversarial attacks. Our work lays a foundation for these emerging applications by emphasizing the role of randomized response within the broader differential privacy framework, paving the way for advancements in secure medical data sharing and analysis. In this paper, we analyze the classical concept of a randomized response and investigate how it relates to the fundamental concept of differential privacy. Our approach is both mathematical and algorithmic in nature, and our purpose is twofold. On the one hand, we provide a formal and precise definition of differential privacy within a natural and convenient probabilistic—statistical framework. On the other hand, we position a randomized response as a special yet significant instance of differential privacy, demonstrating its utility in preserving individual privacy in sensitive data scenarios. To substantiate our findings, we include key theoretical proofs and provide indicative simulations, accompanied by open-access code to facilitate reproducibility and further exploration.

Playing with Privacy: Exploring the Social Construction of Privacy Norms Through a Card Game

Investigating digital privacy behavior requires consideration of its contextual nuances and the underlying social norms. This study delves into users' joint articulation of such norms by probing their implicit assumptions and "common sense" surrounding privacy conventions. To achieve this, we introduce Privacy Taboo, a card game designed to serve as a playful breaching interview method, fostering discourse on unwritten privacy rules. Through nine interviews involving pairs of participants (n=18), we explore the decision-making and collective negotiation of privacy's vagueness. Our findings demonstrate individuals' ability to articulate their information needs when consenting to fictive data requests, even when contextual cues are limited. By shedding light on the social construction of privacy, this research contributes to a more comprehensive understanding of usable privacy, thereby facilitating the development of democratic privacy frameworks. Moreover, we posit Privacy Taboo as a versatile tool adaptable to diverse domains of application and research.

Privacy and Fairness Analysis in the Post-Processed Differential Privacy Framework

Privacy and Fairness Analysis in the Post-Processed Differential Privacy Framework

Data privacy in the era of AI: Navigating regulatory landscapes for global businesses

The convergence of artificial intelligence (AI) and data privacy has created a pivotal challenge for global businesses navigating complex regulatory landscapes. As AI systems increasingly depend on vast datasets to deliver insights and drive innovation, concerns about data protection, algorithmic transparency, and compliance with privacy laws have intensified. The global regulatory environment, encompassing frameworks such as the European Union’s General Data Protection Regulation (GDPR), California’s Consumer Privacy Act (CCPA), and China’s Personal Information Protection Law (PIPL), presents a fragmented legal landscape that requires careful navigation. This paper examines the multifaceted challenges businesses face in aligning AI adoption with regulatory compliance while maintaining ethical standards. Key concerns include managing cross-border data transfers, ensuring data minimization, addressing algorithmic biases, and safeguarding consumer rights in automated decision-making processes. Furthermore, the need for global harmonization of privacy standards is emphasized, given the inconsistencies in regulations across jurisdictions. Actionable insights are provided for businesses to adapt and thrive in this regulatory environment. These include the implementation of privacy-by-design in AI systems, the adoption of advanced data protection technologies like federated learning and differential privacy, and leveraging AI to enhance compliance processes, such as automated data audits and real-time breach detection. The paper also advocates for collaborative efforts among governments, industry stakeholders, and regulators to establish a cohesive framework for AI and data privacy. By strategically addressing these challenges, businesses can build trust with consumers, mitigate legal risks, and unlock AI’s transformative potential in a privacy-centric era.

GDPR vs. Weakly Protected Parties in Other Countries

The General Data Protection Regulation (GDPR) establishes strong privacy rights for EU residents. However, many countries, including the United States, Asia, and Africa, lack similar protections. This creates challenges for multinational corporations and individuals navigating fragmented global data protection frameworks. This study examines the evolution, principles, and extraterritorial effects of GDPR, comparing it with weaker regulatory frameworks globally. Using qualitative and doctrinal research and document analysis, it identifies enforcement gaps, compliance costs, and operational difficulties in cross-border data transfers. Findings emphasize the need for consistent global standards to address the digital divide and ensure fair data practices. Recommendations highlight multilateral cooperation to develop an interoperable and ethical digital ecosystem. While GDPR offers a foundational model, achieving global harmonization requires collective international commitment. The study concludes with actionable suggestions to strengthen global privacy frameworks and protect vulnerable parties in weakly regulated regions.

INFLUENCE OF WEARABLE HEALTH TECHNOLOGY ON PREVENTIVE HEALTHCARE IN HOSPITALIZED PATIENTS

Background: Wearable health technology has revolutionized modern healthcare by enabling real-time monitoring and proactive interventions. These devices are increasingly used in various healthcare settings to improve outcomes such as activity levels, sleep quality, and hospital readmission rates. However, there is limited evidence regarding their effectiveness in hospitalized patients, a population with unique and intensive healthcare needs. This study aimed to evaluate the impact of wearable technology in enhancing preventive healthcare outcomes among hospitalized individuals. Objective; To assess the effectiveness of wearable health technology in improving activity levels, sleep quality, and reducing hospital readmissions among hospitalized patients across three distinct groups: chronic illness, post-surgical, and rehabilitation. Methods: This six-month study (December 2023 to May 2024) was conducted in four tertiary care hospitals in Punjab, Pakistan, using a mixed-methods approach. A total of 300 participants were purposively recruited and divided into chronic illness (n=120), post-surgical (n=100), and rehabilitation (n=80) groups. Wearable devices monitored key health indicators, including activity levels, sleep quality, and readmission rates. Quantitative data were collected through wearable device metrics and hospital records, while qualitative data were gathered via interviews with patients and healthcare providers. Statistical analysis was performed using descriptive and inferential methods, with thematic analysis employed for qualitative data. Results: Among the chronic illness group, 78% reported improved activity levels, 72% better sleep quality, and 69.6% fewer hospital readmissions. The post-surgical group showed similar improvements, with 70% reporting enhanced activity levels, 68% better sleep quality, and 62% reduced readmissions. The rehabilitation group demonstrated slightly lower outcomes, with 60% improving activity levels, 57.6% reporting better sleep, and 52.8% fewer readmissions. Across all groups, females (68%-70%) and patients aged 31-50 years (43%-49%) showed higher engagement and improved outcomes compared to males and older age groups. Conclusion: Wearable health technology demonstrated significant potential in enhancing preventive healthcare outcomes among hospitalized patients. Improvements in activity levels, sleep quality, and reduced readmissions were most pronounced in the chronic illness group. However, adherence challenges and data privacy concerns highlight the need for patient-centered designs and robust privacy frameworks. These findings emphasize the utility of wearable devices in optimizing hospital care, supporting the broader integration of technology into healthcare systems.

Data Privacy in the Age of Cloud: Ethical Considerations and Impacts on Society

The widespread adoption of cloud computing has fundamentally transformed how organizations manage and protect data, introducing complex privacy challenges alongside unprecedented opportunities. This comprehensive article explores the ethical considerations and societal impacts of data privacy in cloud environments, examining how organizations navigate consent mechanisms, data ownership, and regulatory compliance. The article investigates the evolution of data collection practices, the implementation of privacy frameworks, and the critical role of corporate responsibility in maintaining stakeholder trust. By examining industry research and real-world implementations, this article demonstrates the intricate relationship between technical innovation, ethical governance, and societal expectations in cloud-based data privacy, highlighting the importance of balanced approaches that protect individual privacy while enabling organizational growth and innovation.

Schrems III avagy mi lesz veled transzatlanti adattovábbítás? - Gondolatkísérlet az EU-USA Adatvédelmi Keretrendszer Határozat érvényességéről

The United States of America and Europe are each other's most important cooperative partners. This naturally includes the economic relationship based on the transfer of personal data. Over the past nearly decade, this transatlantic data transfer has operated amid continuous legal uncertainties due to the repeated invalidation of the European Commission's adequacy decisions and issues related to the applicability of standard contractual clauses. The problem appears to be currently resolved thanks to the EU-US Data Privacy Framework Decision. However, this Commission decision has already been challenged, and a new procedure is expected to begin this year. The purpose of this study is to conduct a thought experiment to examine the validity of the mentioned decision, which could serve as guidance for future legal practice and provide forecasts for economic actors about expected developments. The paper's conclusion is that due to the unchanged violation of fundamental rights, the EU-US Data Privacy Framework Decision is likely invalid.

Ensuring Data Privacy in Teen and Children Mental Health Analytics: The Role of Differential Privacy in Data Engineering

This article examines the critical intersection of youth mental health data analytics and privacy preservation technologies, focusing on differential privacy implementations. The article explores the growing mental health crisis among adolescent populations and the subsequent need for data-driven interventions while maintaining stringent privacy protections. It analyzes the effectiveness of differential privacy frameworks in protecting sensitive mental health information while enabling valuable research insights across educational and healthcare settings. The article comprehensively evaluates privacy-preserving mechanisms, including privacy budget management, noise addition techniques, and implementation challenges across various institutional contexts. An examination of real-world applications in school-based mental health programs and healthcare platforms it demonstrates how privacy-enhanced analytics can support effective mental health interventions while maintaining individual privacy and regulatory compliance.

A novel framework for privacy and data protection in internet of things-based energy management systems

A novel framework for privacy and data protection in internet of things-based energy management systems

Cascade genetic testing in hereditary cancer: exploring the boundaries of the Italian legal framework.

Despite its clinical value, cascade genetic testing (CGT) in hereditary cancer syndromes remains underutilized for a number of reasons, including ineffective family communication of genetic risk information. Therefore, alternative strategies are being explored to improve CGT uptake rates; one such strategy is direct contact with at-risk relatives by healthcare professionals with proband consent. It is unclear how Italian laws and regulations pertaining to CGT-including the EU General Data Protection Regulation (GDPR)-should be understood and implemented in the context of such alternative strategies. The authors constructed a hypothetical case about CGT, reviewed laws and regulations on informed consent, privacy, and the right not to know, and analyzed how those laws and regulations might apply to different communicative strategies relevant to the case and aimed at supporting CGT. A constitutionally consistent reading of Italian law and of the GDPR, an integral part of the Italian privacy framework, suggests that multiple communicative approaches may be legally permissible in Italy to support the CGT process. This includes direct contact by healthcare professionals with proband consent, provided certain conditions are met. Understanding the effectiveness of such approaches in improving CGT uptake will require further research efforts.

"I inherently just trust that it works": Investigating Mental Models of Open-Source Libraries for Differential Privacy

Differential privacy (DP) is a promising framework for privacy-preserving data science, but recent studies have exposed challenges in bringing this theoretical framework for privacy into practice. These tensions are particularly salient in the context of open-source software libraries for DP data analysis, which are emerging tools to help data stewards and analysts build privacy-preserving data pipelines for their applications. While there has been significant investment into such libraries, we need further inquiry into the role of these libraries in promoting understanding of and trust in DP, and in turn, the ways in which design of these open-source libraries can shed light on the challenges of creating trustworthy data infrastructures in practice. In this study, we use qualitative methods and mental models approaches to analyze the differences between conceptual models used to design open-source DP libraries and mental models of DP held by users. Through a two-stage study design involving formative interviews with 5 developers of open-source DP libraries and user studies with 17 data analysts, we find that DP libraries often struggle to bridge the gaps between developer and user mental models. In particular, we highlight the tension DP libraries face in maintaining rigorous DP implementations and facilitating user interaction. We conclude by offering practical recommendations for further development of DP libraries.

ANEL: a novel efficient and lightweight authentication scheme for enhancing security in Vehicular Ad Hoc Networks (VANETs) using elliptic curve cryptography

Vehicular Ad-hoc Networks (VANETs) offer immense potential for revolutionizing modern transportation systems through real-time communication and interaction between vehicles and roadside infrastructure. These networks can significantly enhance traffic management, improve safety, and support various vehicular services. However, ensuring secure communication in VANETs remains a critical challenge due to the highly sensitive nature of the data exchanged, such as location and personal information. Current authentication schemes, including the ANEL scheme, exhibit vulnerabilities like data interception and tracking risks. In this paper, we propose a novel, efficient, and lightweight authentication scheme that leverages elliptic curve cryptography (ECC) and message mapping techniques to address these security issues. The proposed solution strengthens encryption, enhances privacy protection, and mitigates risks such as unauthorized access and man-in-the-middle attacks. Furthermore, the scheme offers scalability and distinguishes between regular and priority users, making it adaptable to various VANET applications. This approach significantly improves the overall security and privacy framework of VANET systems while ensuring the efficient use of computational resources in vehicular environments.

Local Differential Privacy for correlated location data release in ITS

The ubiquity of location positioning devices has facilitated the implementation of various Intelligent Transportation System (ITS) applications that generate an enormous volume of location data. Recently, Local Differential Privacy (LDP) has been proposed as a rigorous privacy framework that permits the continuous release of aggregate location statistics without relying on a trusted data curator. However, the conventional LDP was built upon the assumption of independent data, which may not be suitable for inherently correlated location data. This paper investigates the quantification of potential privacy leakage in a correlated location data release scenario under a local setting, which has not been addressed in the literature. Our analysis shows that the privacy guarantee of LDP could be degraded in the presence of spatial–temporal and user correlations, albeit the perturbation is performed locally and independently by the users. This privacy guarantee is bounded by a privacy barrier that is affected by the intensity of correlations. We derive several important closed-form expressions and design efficient algorithms to compute such privacy leakage in a correlated location data. We subsequently propose a Δ-CLDP model that enhances the conventional LDP by incorporating the data correlations, and design a generic LDP data release framework that renders adaptive personalization of privacy preservation. Extensive theoretical analyses and simulations on scalable real datasets validate the security and performance efficiency of our work.

Running out the digital clock: transatlantic privacy politics and veto points in time

ABSTRACT Why does the European Commission continue to sign onto unstable international data flow agreements with the United States? After two agreements were struck down by the European Court of Justice, and clear signals that a new one could meet a similar fate, the European Commission agreed to the Data Privacy Framework. To make sense of this behavior, we connect traditional work on veto points with work on historical institutionalism to highlight an international negotiation strategy – running out the clock. While traditional veto points literature suggests multiple institutions with varying preferences will limit the set of potential policies or their adoption, we highlight how these dynamics change when considering veto points in time. We showcase our argument by building three historical narratives detailing the negotiation of the successive data flow agreements. Our findings have important implications for the future of the transatlantic privacy regime as well as negotiation dynamics.

A Survey on User Profiling, Data Collection, and Privacy Issues of Internet Services

Users are usually required to share several types of data, including their personal data, as different providers strive to offer high-quality services that are often tailored to end-users’ preferences. However, when it comes to personalizing services, there are several challenges for meeting user’s needs and preferences. For content personalization and delivery of services to end users, services typically create user profiles. When user profiles are created, user data is collected and organized to meet the personalization requirements of the services. In this paper, we provide an overview of current research activities that focus on user profiling and ways to protect user data privacy. The paper presents different types of data that services collect from users on examples of commonly used Internet services. It proposes data categorization as a prerequisite for controlled data sharing between users and Internet services. Furthermore, it discusses how data generalization can be used for anonymization purposes on examples of the proposed data categories. Finally, it gives an overview of the privacy framework being developed and gives guidelines for future work focusing on data generalization methods in order to reduce user privacy risks.

A critical review of machine learning applications in supply chain risk management

This article examines the transformative role of Machine Learning (ML) in Supply Chain Risk Management (SCRM), emphasizing its ability to enhance risk prediction and mitigation through real-time, data-driven insights. It demonstrates how ML applications like demand forecasting, inventory optimization, supplier risk assessment, and fraud detection improve supply chain resilience by analyzing large datasets to identify patterns, predict risks, and recommend proactive actions. However, the article also highlights key challenges, including data quality, availability, and privacy issues, which limit the effectiveness of ML models. Integrating ML with legacy systems poses additional technical and financial difficulties, particularly for smaller businesses. High implementation costs and scalability constraints further hinder widespread adoption. Ethical concerns, such as data bias and privacy, still need to be explored, raising questions about responsible ML use in supply chains. Future research should address these gaps by improving data governance frameworks for accuracy and privacy, developing scalable ML models suited to various supply chain environments, and exploring synergies with technologies like blockchain and the Internet of Things (IoT). These advancements will help realize ML’s full potential, fostering more agile, transparent, and resilient supply chains capable of navigating complex global risks. By tackling these challenges, ML can shift SCRM from reactive to proactive, ensuring long-term operational continuity and a competitive edge in the evolving global market.

Designing a utility evaluation framework for synthetic health data

ObjectivesSynthetic data (SD) promises to unlock health data for training, research, and innovation. However, where utility evaluation is performed, it is applied ad-hoc for a single task of interest. We produce an initial design for a robust benchmark across a range of tasks. ApproachWe undertook several projects as a prototyping experiment to gather requirements. These projects replicate previous studies performed on the Medical Information Mart for Intensive Care — a dataset used in more than 4,000 studies. We refine definitions, identify personas, draft a user statement, and collect requirements. ResultsDefinitions: We define utility as an extrinsic measure of SD on a larger system, most often through comparison to system performance on real data. This contrasts with fidelity, which measures the accuracy of SD through direct comparison to real data. Personas: Data custodian, User of SD, SD researcher. User statement: As a technical stakeholder, I need a reliable way to measure the utility of datasets and a benchmark to compare generation techniques. Requirements: SD researchers can focus on generation not evaluation; Supports comparison and leaderboards; Based on relevant and applications; Comprehensive across study types and applications; Future proof for population research requiring linking. ConclusionWe propose the following design: Data pipelines follow an extract-generate-evaluate workflow. Study types include cross-sectional and longitudinal. Applications include predictive modelling and clinical research. This results in a comprehensive utility benchmarking suite that complements current frameworks for fidelity and privacy of SD.

A Framework for Adversarial Streaming Via Differential Privacy and Difference Estimators

Classical streaming algorithms operate under the (not always reasonable) assumption that the input stream is fixed in advance. Recently, there is a growing interest in designing robust streaming algorithms that provide provable guarantees even when the input stream is chosen adaptively as the execution progresses. We propose a new framework for robust streaming that combines techniques from two recently suggested frameworks by Hassidim et al. (NeurIPS 2020) and by Woodruff and Zhou (FOCS 2021). These recently suggested frameworks rely on very different ideas, each with its own strengths and weaknesses. We combine these two frameworks into a single hybrid framework that obtains the “best of both worlds”, thereby solving a question left open by Woodruff and Zhou.

Contextual Acceptance of COVID-19 Mitigation Mobile Apps in the United States: Mixed Methods Survey Study on Postpandemic Data Privacy.

The COVID-19 pandemic gave rise to countless user-facing mobile apps to help fight the pandemic ("COVID-19 mitigation apps"). These apps have been at the center of data privacy discussions because they collect, use, and even retain sensitive personal data from their users (eg, medical records and location data). The US government ended its COVID-19 emergency declaration in May 2023, marking a unique time to comprehensively investigate how data privacy impacted people's acceptance of various COVID-19 mitigation apps deployed throughout the pandemic. This research aims to provide insights into health data privacy regarding COVID-19 mitigation apps and policy recommendations for future deployment of public health mobile apps through the lens of data privacy. This research explores people's contextual acceptance of different types of COVID-19 mitigation apps by applying the privacy framework of contextual integrity. Specifically, this research seeks to identify the factors that impact people's acceptance of data sharing and data retention practices in various social contexts. A mixed methods web-based survey study was conducted by recruiting a simple US representative sample (N=674) on Prolific in February 2023. The survey includes a total of 60 vignette scenarios representing realistic social contexts that COVID-19 mitigation apps could be used. Each survey respondent answered questions about their acceptance of 10 randomly selected scenarios. Three contextual integrity parameters (attribute, recipient, and transmission principle) and respondents' basic demographics are controlled as independent variables. Regression analysis was performed to determine the factors impacting people's acceptance of initial data sharing and data retention practices via these apps. Qualitative data from the survey were analyzed to support the statistical results. Many contextual integrity parameter values, pairwise combinations of contextual integrity parameter values, and some demographic features of respondents have a significant impact on their acceptance of using COVID-19 mitigation apps in various social contexts. Respondents' acceptance of data retention practices diverged from their acceptance of initial data sharing practices in some scenarios. This study showed that people's acceptance of using various COVID-19 mitigation apps depends on specific social contexts, including the type of data (attribute), the recipients of the data (recipient), and the purpose of data use (transmission principle). Such acceptance may differ between the initial data sharing and data retention practices, even in the same context. Study findings generated rich implications for future pandemic mitigation apps and the broader public health mobile apps regarding data privacy and deployment considerations.