Information Privacy Research Articles

Inclusion of people with disabilities in Chilean health policy: a policy analysis

BackgroundAround 18% of the population in Chile has disabilities. Evidence shows that this population has greater healthcare needs, yet they face barriers to accessing healthcare due to health system failures. This paper aims to assess the inclusion of people with disabilities in health policy documents and to explore the perceptions of key national stakeholders regarding the policy context, policy processes, and actors involved.MethodsA policy content analysis was conducted of 12 health policy documents using the EquiFrame framework, adapted to assess disability inclusion. Documents were reviewed and rated on their quality of commitment against 21 core concepts of human rights in the framework. Key national stakeholders (n = 15) were interviewed, and data were thematically analysed under the Walt and Gilson Policy Analysis Triangle, using NVivo R1.ResultsCore human rights concepts of disability were mentioned at least once in nearly all health policy documents (92%). However, 50% had poor policy commitments for disability. Across policies, Prevention of health conditions was the main human rights concept reflected, while Privacy of information was the least referenced concept. Participants described a fragmented disability movement and health policy, related to a dominant biomedical model of disability. It appeared that disability was not prioritized in the health policy agenda, due to ineffective mainstreaming of disability by the Government and the limited influence and engagement of civil society in policy processes. Moreover, the limited existing policy framework on disability inclusion is not being implemented effectively. This implementation gap was attributed to lack of financing, leadership, and human resources, coupled with low monitoring of disability inclusion.ConclusionsImprovements are needed in both the development and implementation of disability-inclusive health policies in Chile, to support the achievement of the right to healthcare for people with disabilities and ensuring that the health system truly “leaves no one behind”.

Blockchain-based proxy re-encryption access control method for biological risk privacy protection of agricultural products

In today’s globalized agricultural system, information leakage of agricultural biological risk factors can lead to business risks and public panic, jeopardizing corporate reputation. To solve the above problems, this study constructs a blockchain network for agricultural product biological risk traceability based on agricultural product biological risk factor data to achieve traceability of biological risk traceability data of agricultural product supply chain to meet the sustainability challenges. To guarantee the secure and flexible sharing of agricultural product biological risk privacy information and limit the scope of privacy information dissemination, the blockchain-based proxy re-encryption access control method (BBPR-AC) is designed. Aiming at the problems of proxy re-encryption technology, such as the third-party agent being prone to evil, the authorization judgment being cumbersome, and the authorization process not automated, we design the proxy re-encryption access control mechanism based on the traceability of agricultural products’ biological risk factors. Designing an attribute-based access control (ABAC) mechanism based on the traceability blockchain for agricultural products involves defining the attributes of each link in the agricultural supply chain, formulating policies, and evaluating and executing these policies, deployed in the blockchain system in the form of smart contracts. This approach achieves decentralization of authorization and automation of authority judgment. By analyzing the data characteristics within the agricultural product supply chain to avoid the malicious behavior of third-party agents, the decentralized blockchain system acts as a trusted third-party agent, and the proxy re-encryption is combined with symmetric encryption to improve the encryption efficiency. This ensures a efficient encryption process, making the system safe, transparent, and efficient. Finally, a prototype blockchain system for traceability of agricultural biological risk factors is built based on Hyperledger Fabric to verify this research method’s reliability, security, and efficiency. The experimental results show that this research scheme’s initial encryption, re-encryption, and decryption sessions exhibit lower computational overheads than traditional encryption methods. When the number of policies and the number of requests in the access control session is 100, the policy query latency is less than 400 ms, the request-response latency is slightly more than 360ms, and the data uploading throughput is 48.7 tx/s. The data query throughput is 81.8 tx/s, the system performance consumption is low and can meet the biological risk privacy protection needs of the agricultural supply chain. The BBPR-AC method proposed in this study provides ideas for achieving refined traceability management in the agricultural supply chain and promoting digital transformation in the agricultural industry.

The effect of political environment on security and privacy of contact tracing apps evaluation

PurposeContact tracing apps have emerged to collect data and mitigate the spread of infectious diseases. However, privacy and security concerns have caused individuals to hesitate to adopt these solutions. Our objective is to evaluate the role of the political environment, information privacy, security and users’ intentions to use contact tracing apps.Design/methodology/approachWe scraped the digital app store and collected 399 relevant reviews and other data from 21 contact tracing apps in the USA. A semi-supervised machine learning model was developed to extract information on privacy and security aspects from the reviews.FindingsOur findings show a positive connection between security controls and user adoption, as reflected in star ratings. Users residing in states with blue political environments tend to assign higher ratings to apps, especially when robust security controls are in place. These findings confirm the influence of the political environment on the adoption of contact tracing apps. In times of a pandemic, our findings suggest that users prioritize security over privacy concerns, emphasizing the critical role of strong security features in promoting app acceptance.Originality/valueThis paper emphasizes the political environment of the state offering the app intersects with concerns about security and privacy as well as the effectiveness of security and privacy measures, influencing the app’s ratings. Also, it shows the importance of understanding and addressing the role of the political environment when designing and promoting such public health tools, regardless of the specific disease or outbreak.

Joint bidding strategy of multi‐virtual power plant operators

AbstractThe distributed generation of new energy can effectively use the local endemic clean new energy and provide green power. However, due to the small scale and scattered layout of distributed generation resources, it is difficult to participate in the economic dispatch of power systems and even the competition of the power market. Virtual Power Plant (VPP) can act as a carrier of Distributed Energy Resource (DER) to manage its internal energy, to carry out combined bidding in the day‐ahead energy market and the regulation market. When virtual power plants coordinate multiple distributed energy resources to participate in electricity market transactions, the degree of disclosure of their internal privacy information will produce different trading strategies. A Stackelberg game model between multiple virtual power plant aggregators and virtual power plant operators is constructed, and the Kriging model is combined to protect the privacy information in the transactions of virtual power plant operators. Energy management for distributed generation is carried out, and the profit situation of virtual power plant operators under different strategies is analyzed.

Enhancing medical image classification via federated learning and pre-trained model

The precise classification of medical images is crucial in various healthcare applications, especially in fields like disease diagnosis and treatment planning. In recent times, machine-intelligent models are desired to work in remote settings. However, the potential privacy concerns that arise from sharing confidential patient information to train traditional centralized machine learning models cannot be ignored. Federated learning (FL) offers a promising method for collaborative training on distributed data held by various entities, ensuring the privacy of patient information. This study evaluated the efficiency of the pre-trained models in the FL environment for medical image classification. The Convolutional Neural Network (CNN) model with Gray-Level Co-occurrence Matrix (GLCM) and Local Binary Patterns (LBP), along with the EfficientNet model, are being used as the local models. The trainable parameters from the local models are fed as input for building the global model. Pre-trained models trained on extensive datasets, possess valuable characteristics that can be utilized by FL models trained on proprietary datasets. Implementing this method can improve the efficacy and precision of FL models while also ensuring data confidentiality. The proposed model is evaluated using two distinct medical imaging datasets: Magnetic Resonance Image(MRI) and Computed Tomography (CT) scan images. The research highlights the advantages of utilizing pre-trained models in federated learning for medical image classification (MIC). The model’s performance is assessed acrossseveral assessment criteria, demonstrating the model exhibited a satisfactory accuracy rate of 97.4% and 98.8% for MRI and CT scan images, respectively. The model is evaluated concerning to Diagnostic Odds Ratio (DOR), where the proposed global model has exhibited 1164.54 for the MRI images and 6825.17 for CT scan images, and the values have outperformed the pretrained model.

Securing Digital Identities: The Synergy of Information Technology Security, Trust, and Privacy

Securing Digital Identities: The Synergy of Information Technology Security, Trust, and Privacy

‘No status – no census!’ The causes and consequences of the 1971 and 1981 Northern Ireland census boycotts

ABSTRACT Despite Northern Ireland having been established on the basis of statistics from the 1911 census and the centrality of religious demography to many accounts of its conflict, little has been written about the political history of census-taking there. This paper contributes to addressing this oversight by exploring two related controversies in that history: the 1971 and 1981 census boycotts. Through use of archive materials, it demonstrates how concerns about the privacy of personal information collected by the census against the backdrop of the Troubles, were mixed with broader motivations whereby the census was seen as a symbolic target for protest and boycott in the context of discrimination against Catholics and the republican movement’s campaign for political status for its prisoners, with the 1981 boycott taking place during the hunger strike of that year. Despite a perhaps surprisingly low rate of overall non-response to the census given calls for it to be boycotted, the boycotts did have a significant impact and a legacy that lasted into the 1990s—by which time republicans’ attitude towards the census had changed for the positive.

Intimate harms and menstrual cycle tracking apps

Menstrual cycle tracking applications (‘apps’) are smartphone or tablet apps that allow users to log data pertaining to their period. Using a lens of privacy focussed on intimacy, it will be argued that the control-based harms and intimate harms emerging from these apps require moving from an information privacy law model based on control to one that acknowledges the deeper connection between intimacy and privacy. We examine the privacy policies of 20 menstrual cycle tracking apps to investigate how the control-based protections of the Privacy Act apply. Our findings demonstrate that there are many deficiencies in app privacy policies which give rise to critical questioning about the application of the Australian Privacy Act’s control approach. We argue that the current gender-agnostic approach of information privacy law's control approach does not adequately protect app users and their intimate information. Intimate harms rethink the application of information privacy law by extending its reach beyond the traditional control harms contemplated by the Act and examine how menstrual cycle tracking apps disrupt users’ intimate spheres and relationships. To adequately protect app users from these deeper intimate harms, we contend that information privacy law moves beyond the procedural-based control approach to an information privacy model that is relational, context-dependant and acknowledges the connection between intimacy and privacy.

PrivateGaze: Preserving User Privacy in Black-box Mobile Gaze Tracking Services

Eye gaze contains rich information about human attention and cognitive processes. This capability makes the underlying technology, known as gaze tracking, a critical enabler for many ubiquitous applications and has triggered the development of easy-to-use gaze estimation services. Indeed, by utilizing the ubiquitous cameras on tablets and smartphones, users can readily access many gaze estimation services. In using these services, users must provide their full-face images to the gaze estimator, which is often a black box. This poses significant privacy threats to the users, especially when a malicious service provider gathers a large collection of face images to classify sensitive user attributes. In this work, we present PrivateGaze, the first approach that can effectively preserve users' privacy in black-box gaze tracking services without compromising gaze estimation performance. Specifically, we proposed a novel framework to train a privacy preserver that converts full-face images into obfuscated counterparts, which are effective for gaze estimation while containing no privacy information. Evaluation on four datasets shows that the obfuscated image can protect users' private information, such as identity and gender, against unauthorized attribute classification. Meanwhile, when used directly by the black-box gaze estimator as inputs, the obfuscated images lead to comparable tracking performance to the conventional, unprotected full-face images.

Five years with the GDPR: an empirical study emphasising information privacy and the consumer

Consumers’ privacy rights have been enshrined in law, long before information systems and the Internet was brought to life. In 2018, stricter regulations relating to information privacy came into force, named the General Data Protection Regulation (GDPR). Using elements of Roger’s diffusion of innovations theory, we investigated the research question: How has five years of the GDPR influenced consumer’s knowledge, attitude, and practice of their enhanced rights? We draw on empirical data collected in Norway through four online survey questionnaires over five years (N=1293). Quantitative (descriptive statistics) and qualitative analyses (manual cluster text mining) were performed to obtain a state-of-the-art mapping of insights on consumers and their information privacy. Our findings show that the respondents’ answers remained similar over the years, and that the GDPR has not had a significant influence on the consumer. The respondents demonstrated a high degree of knowledge regarding both the regulation and technology, such as cookies. Their attitude was sceptical, as they valued their enhanced rights but questioned the feasibility. Regarding their practice, our findings reveal diversity. Some respondents took careful actions to protect their privacy, while most did not. The present paper should be interesting to both the industry (practitioners) and academia (researchers).

User Acceptance of Blockchain Technology in Financial Applications: Information Security, Technology Awareness and Privacy Aspects

Blockchain technology is now an advanced and innovative database technology and the attributes of blockchains are apparent in a variety of industries, especially in the financial industry. One of the most famous blockchain use cases, cryptocurrencies, has provoked much interest in social network users and customers. According to CoinMarketCap’s information, the global crypto market capitalization has reached around USD 2.37 T and there are around 9975 different cryptocurrencies available in the market. Despite the fact that academia and industry have paid much attention towards the blockchain direction, there is not much research on the factors that influence customer acceptability. This paper studies blockchains from a different angle, probing the factors prompting customers to use financial applications that utilize blockchain technology. We established the model and sorted the individual factors of perceived information security, technology awareness and privacy and found that users’ acceptance is significantly affected by information security and technology awareness, while privacy does not significantly influence users. According to the findings, we provide useful insights for application developers, conclude by presenting the limitations of the research and provide guidelines for future research.

Effective Privacy Protection Strategies for Pregnancy and Gestation Information From Electronic Medical Records: Retrospective Study in a National Health Care Data Network in China.

Pregnancy and gestation information is routinely recorded in electronic medical record (EMR) systems across China in various data sets. The combination of data on the number of pregnancies and gestations can imply occurrences of abortions and other pregnancy-related issues, which is important for clinical decision-making and personal privacy protection. However, the distribution of this information inside EMR is variable due to inconsistent IT structures across different EMR systems. A large-scale quantitative evaluation of the potential exposure of this sensitive information has not been previously performed, ensuring the protection of personal information is a priority, as emphasized in Chinese laws and regulations. This study aims to perform the first nationwide quantitative analysis of the identification sites and exposure frequency of sensitive pregnancy and gestation information. The goal is to propose strategies for effective information extraction and privacy protection related to women's health. This study was conducted in a national health care data network. Rule-based protocols for extracting pregnancy and gestation information were developed by a committee of experts. A total of 6 different sub-data sets of EMRs were used as schemas for data analysis and strategy proposal. The identification sites and frequencies of identification in different sub-data sets were calculated. Manual quality inspections of the extraction process were performed by 2 independent groups of reviewers on 1000 randomly selected records. Based on these statistics, strategies for effective information extraction and privacy protection were proposed. The data network covered hospitalized patients from 19 hospitals in 10 provinces of China, encompassing 15,245,055 patients over an 11-year period (January 1, 2010-December 12, 2020). Among women aged 14-50 years, 70% were randomly selected from each hospital, resulting in a total of 1,110,053 patients. Of these, 688,268 female patients with sensitive reproductive information were identified. The frequencies of identification were variable, with the marriage history in admission medical records being the most frequent at 63.24%. Notably, more than 50% of female patients were identified with pregnancy and gestation history in nursing records, which is not generally considered a sub-data set rich in reproductive information. During the manual curation and review process, 1000 cases were randomly selected, and the precision and recall rates of the information extraction method both exceeded 99.5%. The privacy-protection strategies were designed with clear technical directions. Significant amounts of critical information related to women's health are recorded in Chinese routine EMR systems and are distributed in various parts of the records with different frequencies. This requires a comprehensive protocol for extracting and protecting the information, which has been demonstrated to be technically feasible. Implementing a data-based strategy will enhance the protection of women's privacy and improve the accessibility of health care services.

Ethical Challenges in Information Disclosure and Decision-making in Prenatal Testing: A Focus Group Study of Chinese Health Professionals in Maternal and Child Health Services.

The international community has proposed a comprehensive strategy to prevent congenital abnormalities. And China, with a high incidence of congenital diseases, has implemented measures including prenatal screening and diagnosis to reduce the morbidity of congenital abnormalities. However, ethical challenges arise in the practice of prenatal screening and diagnosis among healthcare professionals. Five focus group discussions were conducted with twenty-four health professionals working in maternal and child health services in Hunan Province, China, to explore the ethical challenges they encountered in prenatal testing decision-making and information disclosure practices, as well as their views on these challenges. Participants were selected through purposive sampling, ensuring maximum demographic diversity. Three main themes were identified: 1) balancing between information disclosure and privacy protection; 2) patient-oriented decision-making and tensions within family-oriented decision-making; 3) the disparity between the limited help clinical ethics committees (CECs) can provide and professionals' need for CECs. Ethical norms for information disclosure and autonomous decision-making within prenatal screening and diagnostic institutions must be established. Utilizing CECs is crucial to guide professionals in delivering prenatal testing services while simultaneously focusing on targeted improvement of communication skills among these professionals.

Achieving High Accuracy in Android Malware Detection through Genetic Programming Symbolic Classifier

The detection of Android malware is of paramount importance for safeguarding users’ personal and financial data from theft and misuse. It plays a critical role in ensuring the security and privacy of sensitive information on mobile devices, thereby preventing unauthorized access and potential damage. Moreover, effective malware detection is essential for maintaining device performance and reliability by mitigating the risks posed by malicious software. This paper introduces a novel approach to Android malware detection, leveraging a publicly available dataset in conjunction with a Genetic Programming Symbolic Classifier (GPSC). The primary objective is to generate symbolic expressions (SEs) that can accurately identify malware with high precision. To address the challenge of imbalanced class distribution within the dataset, various oversampling techniques are employed. Optimal hyperparameter configurations for GPSC are determined through a random hyperparameter values search (RHVS) method developed in this research. The GPSC model is trained using a 10-fold cross-validation (10FCV) technique, producing a set of 10 SEs for each dataset variation. Subsequently, the most effective SEs are integrated into a threshold-based voting ensemble (TBVE) system, which is then evaluated on the original dataset. The proposed methodology achieves a maximum accuracy of 0.956, thereby demonstrating its effectiveness for Android malware detection.

When HIPAA hurts: legal barriers to texting may reinforce healthcare disparities and disenfranchise vulnerable patients.

Effective health communication between healthcare providers and patients is a cornerstone of quality healthcare. It underpins trust, comprehension, and patient-informed care. Robust research shows that effective communication, including the use of text messaging for communication can improve maternal/fetal and neonatal outcomes and patient satisfaction, particularly among vulnerable patients. Health information privacy laws that do not evolve with technological advances can inadvertently create barriers to effective health communication, reinforcing perinatal disparities. This is particularly true regarding maternal and child health, where the use of text messaging for patient communication has the potential to make a substantial impact on health disparities. This article explores the complex interplay between health information privacy laws and text messaging, highlighting challenges and examining potential solutions. It stresses the need for consistent health information privacy laws that protect the privacy security of health information for pregnant patients and new mothers, while also aligning with evolving communication technologies.

Do patients prefer a human doctor, artificial intelligence, or a blend, and is this preference dependent on medical discipline? Empirical evidence and implications for medical practice.

Today the doctor-patient relationship typically takes place in a face-to-face setting. However, with the advent of artificial intelligence (AI) systems, two further interaction scenarios are possible: an AI system supports the doctor's decision regarding diagnosis and/or treatment while interacting with the patient, or an AI system could even substitute the doctor and hence a patient interacts with a chatbot (i.e., a machine) alone. Against this background, we report on an online experiment in which we analyzed data from N = 1,183 people. The data was collected in German-speaking countries (Germany, Austria, Switzerland). The participants were asked to imagine they had been suffering from medical conditions of unknown origin for some time and that they were therefore visiting a health center to seek advice from a doctor. We developed descriptions of patient-doctor interactions (referred to as vignettes), thereby manipulating the patient's interaction partner: (i) human doctor, (ii) human doctor with an AI system, and (iii) an AI system only (i.e., chatbot). Furthermore, we manipulated medical discipline: (i) cardiology, (ii) orthopedics, (iii) dermatology, and (iv) psychiatry. Based on this 3 × 4 experimental within-subjects design, our results indicate that people prefer a human doctor, followed by a human doctor with an AI system, and an AI system alone came in last place. Specifically, based on these 12 hypothetical interaction situations, we found a significant main effect of a patient's interaction partner on trust, distrust, perceived privacy invasion, information disclosure, treatment adherence, and satisfaction. Moreover, perceptions of trust, distrust, and privacy invasion predicted information disclosure, treatment adherence, and satisfaction as a function of interaction partner and medical discipline. We found that the situation in psychiatry is different from the other three disciplines. Specifically, the six outcome variables differed strongly between psychiatry and the three other disciplines in the "human doctor with an AI system" condition, while this effect was not that strong in the other conditions (human doctor, chatbot). These findings have important implications for the use of AI in medical care and in the interaction between patients and their doctors.

Efficient and Verifiable Range Query Scheme for Encrypted Geographical Information in Untrusted Cloud Environments

With the rapid development of geo-positioning technologies, location-based services have become increasingly widespread. In the field of location-based services, range queries on geographical data have emerged as an important research topic, attracting significant attention from academia and industry. In many applications, data owners choose to outsource their geographical data and range query tasks to cloud servers to alleviate the burden of local data storage and computation. However, this outsourcing presents many security challenges. These challenges include adversaries analyzing outsourced geographical data and query requests to obtain privacy information, untrusted cloud servers selectively querying a portion of the outsourced data to conserve computational resources, returning incorrect search results to data users, and even illegally modifying the outsourced geographical data, etc. To address these security concerns and provide reliable services to data owners and data users, this paper proposes an efficient and verifiable range query scheme (EVRQ) for encrypted geographical information in untrusted cloud environments. EVRQ is constructed based on a map region tree, 0–1 encoding, hash function, Bloom filter, and cryptographic multiset accumulator. Extensive experimental evaluations demonstrate the efficiency of EVRQ, and a comprehensive analysis confirms the security of EVRQ.

Improving security of efficient multiparty quantum secret sharing based on a novel structure and single qubits

Quantum secret sharing is a basic quantum cryptographic primitive, which has a lot of applications in information security and privacy preservation. An efficient multiparty quantum secret sharing protocol (Kuo et al. in EPJ Quantum Technol 10(1):29, 2023) based on a novel structure and single qubits was reported recently. In this paper, we give a cryptanalysis of this protocol and show that it cannot satisfy the security requirement for secret sharing because an unauthorized set of agents can gain access to some information on the dealer’s secret by a special collusion attack. Furthermore, we put forward a way to deal with the security problem.

DGGI: Deep Generative Gradient Inversion with diffusion model

Federated learning is a privacy-preserving distributed framework that facilitates information fusion and sharing among different clients, enabling the training of a global model without exposing raw data. However, the gradient inversion attack that can reconstruct the training data via gradients has posed a significant threat. Prior attack approaches have demonstrated the efficacy of gradient inversion on low-resolution datasets with small batch sizes, which is impractical in real scenarios. To tackle this issue, this paper proposes an innovative and practical gradient inversion method, namely Deep Generative Gradient Inversion (DGGI), which employs the prior knowledge of diffusion models to enhance reconstruction performance on high-resolution datasets and larger batch sizes. Furthermore, a novel group consistency regularization term that constrains the distance between reconstruction and alignment images has been developed to address the issue of spatial variations caused by pre-trained diffusion model. Experiments conducted on both natural and medical image datasets demonstrate that our DGGI method outperforms state-of-the-art baselines in image reconstruction metrics. Furthermore, our approach achieves pixel-level reconstruction and causes leakage of privacy information, even at larger batch sizes or under various defenses, which can aid in the exploration of latent security concerns within information fusion models.

Underrepresented Populations on Public Health Data Dashboards: Design Considerations for Improving Usability and Usefulness.

Representation of racial and ethnic minority groups and sexual and gender minority groups is limited on public health data dashboards. The small size of these populations leads to data being aggregated or suppressed to produce stable estimations and preserve individuals' information privacy. This might, however, limit the usefulness of the represented data for identifying individuals' risk factors and allocating services. To explore public health practitioners' concerns about underrepresented populations on public health data dashboards that emerged during a usability evaluation study. 20 public health practitioners from New York State, US. Virtual Zoom meetings were conducted to monitor the participants' use of a public health dashboard and ask about their experience using it. The collected data, in the form of researchers' notes and audio transcripts, were analyzed using the thematic analysis approach. Participants were very concerned about the underrepresentation of sexual and gender minority groups and racial and ethnic minority groups on public health data dashboards. Four themes emerged regarding the consequences of the underrepresentation: (1) misinterpretation of risk for underrepresented groups, (2) exacerbating the mistrust between underrepresented populations and the government, (3) a potential disservice to underserved populations if the data is used as the basis for allocating resources, and (4) and unknown impact for the individuals whose demographic information is unknown. We propose considerations for displaying underrepresented populations on public health data dashboards to improve the utility of the represented data.