Privacy Preserving Attribute-Focused Anonymization Scheme for Healthcare Data Publishing

Nowadays Privacy Preserving in Data Publishing (PPDP) has rapidly growth into the research contents in data protection field and also it has rapidly grown in publication of personal data. Recent years in data publishing is exceptionally analytical, because, how to economically preserve numerical and categorical sensitive attribute. However, different tender have been designed for privacy preserving to protect numerical sensitive attribute in data publishing, Like k-anonymity, l-diversity, t-closeness, (epsilon, m)-anonymity another methods are implemented for protecting the privacy of data provider. In this paper, we propose a review of various multiple numerical and categorical sensitive attribute for preserving privacy and survey current existing techniques. Yet, we discuss the future instructions of privacy preserving in data publishing, that is we suggest modern technique for “A new enhanced slicing method for both numerical and categorical sensitive attribute via advanced clustering algorithm” that improving the privacy with less information loss, membership, attribute , identity disclosure and error ratio and also calculate distance between two attributes for categorical attribute. This technique suitable for both categorical and numerical sensitive attributes.

With the constant increase of social-network data published, the privacy preservation becomes more and more important. Although some literature algorithms apply K-anonymity to the relational data to prevent an adversary from significantly perpetrating privacy breaches, the inappropriate choice of K has a big impact on the quality of privacy protection and data utility. We propose a technique named Relationship Privacy Preservation based on Compressive Sensing (RPPCS) in this paper to anonymize the relationship data of social networks. The network links are randomized from the recovery of the random measurements of the sparse relationship matrix to both preserve the privacy and data utility. Two comprehensive sets of real-world relationship data on social networks are applied to evaluate the performance of our anonymization technique. Our performance evaluations based on Collaboration Network and Gnutella Network demonstrate that our scheme can better preserve the utility of the anonymized data compared to peer schemes. Privacy analysis shows that our scheme can resist the background knowledge attack.

Transparent sharing of digital health data: A call to action

The trends of data mining on healthcare data for improving medical services have increased because of the electronic healthcare record(EHR) system, which collects a massive amount of data on a daily basis. In the current scenario, hospital maintains its EHR system and stores the detailed information of patients. Data mining for healthcare improvement requires the data from all the EHR systems located at a different location to be stored at the central data mining server. Collection of healthcare data at some untrusted central data mining server raises privacy threats. Healthcare data contains patients’ private information and sharing this information for data mining creates privacy issues. Most of the previous research either focused on k-anonymity technique which causes information loss and decreases data mining accuracy or privacy preserving data mining which is focused on only specific data mining technique. We adopt source anonymous technique as privacy preserving scheme and present a novel scheme for healthcare data collection and mining in this paper. Our scheme collects data from all EHR systems without any information loss and stores at a single central data mining server, also ensuring privacy is preserved. Central data mining server helps to analyze the collected data with different data mining techniques (Association rule mining, Classification, Clustering, etc.) without the involvement of EHR systems. Our scheme is collusion resilient against central data mining server and EHR systems. Theoretical and experimental analysis show the efficiency of our scheme in terms of computation and communication cost. The experimental results using Heart disease dataset show the advantage to EHR systems using the proposed approach in terms of disease prediction accuracy.

The Adoption of Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT), cloud services, web-based software systems, and other wireless sensor devices in the healthcare infrastructure have led to phenomenal improvements and benefits in the healthcare sector. Digital healthcare has ensured early diagnosis of the diseases, greater accessibility, and mass outreach in terms of treatment. Despite this unprecedented success, the privacy and confidentiality of the healthcare data have become a major concern for all the stakeholders. Data breach reports reveal that the healthcare data industry is one of the key targets of cyber invaders. In fact the last few years have registered an unprecedented rise in healthcare data breaches. Hacking incidents and privilege abuse are the most common threats and have exposed sensitive and protected health data. Experts and researchers are working on various techniques, tools, and methods to address the security issues related to healthcare data. In this article, the main focus is on evaluating the impact of research studies done in the context of healthcare data breach reports to identify the contemporary privacy and confidentiality issues of sensitive healthcare data. Analysis of the research studies depicts that there is a need for proactive security mechanisms that will help the healthcare organizations to identify abnormal user behavior while accessing healthcare data. Moreover, studies also suggest that ML techniques would be highly effective in securing the privacy and confidentiality of the healthcare data. Working further on this premise, the present study also proposes a conceptual framework that will secure the privacy and confidentiality of healthcare data proactively. The proposed framework is based on ML techniques to detect deviated user access against Electronic Health Records. Further, fuzzy-based Analytical Network Process (ANP), a multi-criteria decision-making approach, is used to assess the accuracy of the supervised and unsupervised ML approaches for achieving a dynamic digital healthcare data security environment.

The inconceivable ability and common practice to collect personal data as well as the power of data‐driven approaches to businesses, services and security nowadays also introduce significant privacy issues. There have been extensive studies on addressing privacy preserving problems in the data mining community but relatively few have provided supervised control over the anonymization process. Preserving both the value and privacy of the data is largely a non‐trivial task. We present the design and evaluation of a visual interface that assists users in employing commonly used data anonymization techniques for making privacy preserving visualizations. Specifically, we focus on event sequence data due to its vulnerability to privacy concerns. Our interface is designed for data owners to examine potential privacy issues, obfuscate information as suggested by the algorithm and fine‐tune the results per their discretion. Multiple use case scenarios demonstrate the utility of our design. A user study similarly investigates the effectiveness of the privacy preserving strategies. Our results show that using a visual‐based interface is effective for identifying potential privacy issues, for revealing underlying anonymization processes, and for allowing users to balance between data utility and privacy.

The anonymization of Common Data Model (CDM)-converted EHR data is essential to ensure the data privacy in the use of harmonized health care data. However, applying data anonymization techniques can significantly affect many properties of the resulting data sets and thus biases research results. Few studies have reviewed these applications with a reflection of approaches to manage data utility and quality concerns in the context of CDM-formatted health care data. Our intended scoping review aims to identify and describe (1) how formal anonymization methods are carried out with CDM-converted health care data, (2) how data quality and utility concerns are considered, and (3) how the various CDMs differ in terms of their suitability for recording anonymized data. The planned scoping review is based on the framework of Arksey and O'Malley. By using this, only articles published in English will be included. The retrieval of literature items should be based on a literature search string combining keywords related to data anonymization, CDM standards, and data quality assessment. The proposed literature search query should be validated by a librarian, accompanied by manual searches to include further informal sources. Eligible articles will first undergo a deduplication step, followed by the screening of titles. Second, a full-text reading will allow the 2 reviewers involved to reach the final decision about article selection, while a domain expert will support the resolution of citation selection conflicts. Additionally, key information will be extracted, categorized, summarized, and analyzed by using a proposed template into an iterative process. Tabular and graphical analyses should be addressed in alignment with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) checklist. We also performed some tentative searches on Web of Science for estimating the feasibility of reaching eligible articles. Tentative searches on Web of Science resulted in 507 nonduplicated matches, suggesting the availability of (potential) relevant articles. Further analysis and selection steps will allow us to derive a final literature set. Furthermore, the completion of this scoping review study is expected by the end of the fourth quarter of 2023. Outlining the approaches of applying formal anonymization methods on CDM-formatted health care data while taking into account data quality and utility concerns should provide useful insights to understand the existing approaches and future research direction based on identified gaps. This protocol describes a schedule to perform a scoping review, which should support the conduction of follow-up investigations. PRR1-10.2196/46471.

There is a consensus among scientists that the analysis of Big Data in health care (such as electronic health records, patient reported outcomes or in-motion data) can improve clinical research and the quality of care provided to patients. Yet there is little knowledge about the actual effectiveness of Big Data in the health care sector. The aim of this study was to perform a systematic review of the literature in order to determine the extent to which Big Data applications in health care systems have managed to improve patient experiences and clinicians’ behavior as well as the quality of care provided to patients. All searches for relevant articles were performed in the PubMed database. From the 108 potentially relevant articles 12 satisfied the inclusion criteria for this study. The findings showed that in the case of nine articles the researchers reported positive effect of Big Data. However, some negative results were recorded in the case of three articles. The main benefits of Big Data application involved positive behavior change, improved usability and efficient decision support. However, problems were identified for technology acceptance. Most problems occurred in the case of systems processing heterogeneous datasets, patient reported outcomes and in motion data, as opposed to electronic health record systems. The paper concludes by highlighting some areas of investigation where further research is needed to understand the use of Big Data in health care and improve its effectiveness.KeywordsHealth InformaticsBig DataElectronic Health RecordsEffectiveness

Healthcare service delivery, especially in terms of safeguarding personal data, requires ensuring the confidentiality of information. In this regard, establishing cybersecurity systems that ensure information security is highly necessary. The rapid advancement of technologies increases the likelihood of cyberattacks, and particularly, AI-supported threats can cause serious harm in service delivery. In the current era, attacks not only come from humans but also from AI tools, posing threats to information security. Considering that AI technology is expected to further advance in the future, it's evident that this technology could become even more menacing. This is especially pertinent to the healthcare sector. Cyberattacks can lead to breaches in healthcare system data and disrupt service delivery to the extent of paralyzing the healthcare system. Our study, which includes case examples, is a compilation-type research. Within the scope of our research, searches were conducted using the keywords healthcare sector, information security, and cybersecurity on Google Scholar and Web of Science. The most current topic headings intersecting information security with the healthcare sector were examined based on the articles found on the subject. Our study evaluates the following topics in order: information and cyber security concepts, cyber threats and public services, electronic health records and security, major cyber-attacks in the health sector, why healthcare data is attractive for cyberattacks, information security in the artificial intelligence era, and information security policies for Türkiye and other countries in the world. Ransomware holds a significant place among cyberattacks. Therefore, users within the healthcare system are advised to pay particular attention to this issue. Attacks generally occur via email, starting with enticing the user into a cyber-threat through email. Artificial intelligence can also be used to get rid of such spam mails. Hence, it is strongly recommended that users in the healthcare sector undergo training on this matter. These trainings should be conducted regularly and continuously, with the institution's IT center offering an institutional approach in this regard.

Data are central to research, public health, and in developing health information technology (IT) systems. Nevertheless, access to most data in health care is tightly controlled, which may limit innovation, development, and efficient implementation of new research, products, services, or systems. Using synthetic data is one of the many innovative ways that can allow organizations to share datasets with broader users. However, only a limited set of literature is available that explores its potentials and applications in health care. In this review paper, we examined existing literature to bridge the gap and highlight the utility of synthetic data in health care. We searched PubMed, Scopus, and Google Scholar to identify peer-reviewed articles, conference papers, reports, and thesis/dissertations articles related to the generation and use of synthetic datasets in health care. The review identified seven use cases of synthetic data in health care: a) simulation and prediction research, b) hypothesis, methods, and algorithm testing, c) epidemiology/public health research, d) health IT development, e) education and training, f) public release of datasets, and g) linking data. The review also identified readily and publicly accessible health care datasets, databases, and sandboxes containing synthetic data with varying degrees of utility for research, education, and software development. The review provided evidence that synthetic data are helpful in different aspects of health care and research. While the original real data remains the preferred choice, synthetic data hold possibilities in bridging data access gaps in research and evidence-based policymaking.

Technology massification is an acceptable trend for healthcare. Unlike technology massification, big data is crucial in the healthcare sector. This study uses space syntax in assessing the applicability and use of big data in healthcare. Following a rise in data required within the healthcare system, it’s important to apply big data technologies as more information increases in healthcare every day. Securing new data storage steps, modelings, systems, and processes is essential for internal and external use as proposed by this study. To ascertain that storage steps, modeling systems, and processes are essential for the implementation of big data in healthcare, a survey was conducted. The results of findings show big data is required in the healthcare sector. Search engines, publish journals, and social media platforms were used to determine this trend. The trend was classified into internal and external systems. A choice was made accepting the highest occurrence of everyday data input into systems and environmental space used. From the various literature review, a higher volume of text and words used were displayed with the help of keywords. Search quarries represent the internal trend of space-syntax analysis for healthcare and GIS mapping represents the external environment of space-syntax analyzes for non-healthcare. From the applicability of syntax queries as a comparative analysis, higher-volume data is predominant in healthcare-related issues than non-healthcare issues like FIFA competitions. This study, therefore, concluded that there is a need for big data in healthcare to contain increased incoming text or healthcare information.

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