Anonymizing Periodical Releases of SRS Data by Fusing Differential Privacy

Spontaneous reporting systems (SRS) have been developed to collect adverse event records that contain personal demographics and sensitive information like drug indications and adverse reactions. The release of SRS data may disclose the privacy of the data provider. Unlike other microdata, very few anonymyization methods have been proposed to protect individual privacy while publishing SRS data. MS(k, {\theta}*)-bounding is the first privacy model for SRS data that considers multiple individual records, mutli-valued sensitive attributes, and rare events. PPMS(k, {\theta}*)-bounding then is proposed for solving cross-release attacks caused by the follow-up cases in the periodical SRS releasing scenario. A recent trend of microdata anonymization combines the traditional syntactic model and differential privacy, fusing the advantages of both models to yield a better privacy protection method. This paper proposes the PPMS-DP(k, {\theta}*, {\epsilon}) framework, an enhancement of PPMS(k, {\theta}*)-bounding that embraces differential privacy to improve privacy protection of periodically released SRS data. We propose two anonymization algorithms conforming to the PPMS-DP(k, {\theta}*, {\epsilon}) framework, PPMS-DPnum and PPMS-DPall. Experimental results on the FAERS datasets show that both PPMS-DPnum and PPMS-DPall provide significantly better privacy protection than PPMS-(k, {\theta}*)-bounding without sacrificing data distortion and data utility.Comment: 10 pages, 11 figure

Algorithms to anonymize structured medical and healthcare data:A systematic review

Introduction: With many anonymization algorithms developed for structured medical health data (SMHD) in the last decade, our systematic review provides a comprehensive bird’s eye view of algorithms for SMHD anonymization. Methods: This systematic review was conducted according to the recommendations in the Cochrane Handbook for Reviews of Interventions and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Eligible articles from the PubMed, ACM digital library, Medline, IEEE, Embase, Web of Science Collection, Scopus, ProQuest Dissertation, and Theses Global databases were identified through systematic searches. The following parameters were extracted from the eligible studies: author, year of publication, sample size, and relevant algorithms and/or software applied to anonymize SMHD, along with the summary of outcomes. Results: Among 1,804 initial hits, the present study considered 63 records including research articles, reviews, and books. Seventy five evaluated the anonymization of demographic data, 18 assessed diagnosis codes, and 3 assessed genomic data. One of the most common approaches was k-anonymity, which was utilized mainly for demographic data, often in combination with another algorithm; e.g., l-diversity. No approaches have yet been developed for protection against membership disclosure attacks on diagnosis codes. Conclusion: This study reviewed and categorized different anonymization approaches for MHD according to the anonymized data types (demographics, diagnosis codes, and genomic data). Further research is needed to develop more efficient algorithms for the anonymization of diagnosis codes and genomic data. The risk of reidentification can be minimized with adequate application of the addressed anonymization approaches. Systematic Review Registration: [http://www.crd.york.ac.uk/prospero], identifier [CRD42021228200].</p

Efficient Q-Value Zero-Leakage Protection Scheme in SRS Regularly Publishing Private Data

Spontaneous Reporting System (SRS) has been widely established to collect adverse drug events. Thus, SRS promotes the detection and analysis of ADR (adverse drug reactions), such as the FDA Adverse Event Reporting System (FAERS). The SRS data needs to be provided to researchers. Meanwhile, the SRS data is publicly available to facilitate the study of ADR detection and analysis. In general, SRS data contains private information of some individual characteristics. Before the information is published, it is necessary to anonymize private information in the SRS data to prevent disclosure of individual privacy. There are many privacy protection methods. The most classic method for protecting SRS data is called as PPMS. However, in the real world, SRS data is growing dynamically and needs to be published regularly. In this case, PPMS has some shortcomings in the memory consumption, anonymity efficiency, data update and data security. To remove these shortcomings, we propose an Efficient Q-value Zero-leakage protection Scheme in SRS regularly publishing private data, called EQZS. EQZS can deal with almost all of potential attacks. Meanwhile, EQZS removes the shortcomings of PPMS. The experimental results show that our scheme EQZS solves the problem of privacy leakage in SRS regularly publishing private data. Meanwhile, EQZS significantly outperforms PPMS on the efficiency of memory consumption, privacy anonymity and data update

Novel approaches to the detection of substandard medicines

This thesis addresses the serious implications of substandard medicines to public health care and describes attempts to bridge the apparent gap of detecting substandard medicines by exploring new detection approaches in pharmacovigilance and in analytical technologies as well as validating these methods in a field study. Substandard medicines (SSMs) are licensed medicines, that most commonly contain either too little or too much active pharmaceutical ingredient (API). SSMs may be the result of negligence, error and/or low standard of quality control of the manufacturing process (non-good manufacturing accredited status) and/or distribution process (degradation of medicinal products through bad storage). The use of SSMs may result in severe adverse events (AEs) and even death and may promote antimicrobial resistance. Evidence of high increases of SSMs, predominantly in developing countries, reveals that there is a need for easy, rapid and affordable detection tools. Currently, there is no portable screening device in the market that can accurately measure the content of API of medicinal samples. The only available device, currently under development, is PharmaChk, an innovative analytical instrument, that is able to quantify the amount of a number of APIs (e.g. artemisinin, tetracycline) and evaluate their dissolution profile. In collaboration with the Biomechanical Department of Boston University, we conducted further research on this device. The assay for essential antimalarial drug Coartem® (artemether and lumefantrine) was developed and used for a field study in Zimbabwe. In addition to analytical detection, pharmacovigilance signal detection techniques have been shown to be effective in detecting SSMs. Preliminary research (conducted by the WHO Uppsala Monitoring Centre) exists on using data mining algorithms on the WHO Vigibase® data set of global individual case safety reports to identify SSMs. After validation of this pharmacovigilance screening tool and the assay of Coartem® on the PharmaChk device and in order to assess the efficiency of these two detection approaches of SSMs in real world practice, we initiated a field study on Coartem® and its generic versions in Zimbabwe. Malaria is a major health burden in this country with 8,000,000 people at risk (50% of the population). Previous studies have shown that Zimbabweans are at high risk from substandard and falsified medicines, resulting in increased mortality, morbidity, financial strain and long-term antimicrobial resistance. We collected samples from sites of the private health sector as well as from sites that were conveniently accessible. The purchase of samples was performed in 18 cities in areas with high risk for malaria. The quality of purchased samples was tested through qualitative and quantitative measurements using different screening field devices including Raman, Near-Infrared spectrometry and X-Ray Fluorescence as well as spectrophotometer and high performance liquid chromatography (HPLC) analysis for confirmatory analysis in analytical laboratories in Zimbabwe, Switzerland and the United States. Data mining for the antimalarial drug Coartem® identified no excess reporting of AEs in Zimbabwe. Analyses of all screening and confirmatory analytical technologies revealed a good quality of all collected samples. The PharmaChk device demonstrated comparable results of the collected samples to HPLC, which is the gold standard method. The analytical screening tool PharmaChk was able to determine that there was no unexpected risk with essential medicine artemether/lumefantrine in Zimbabwe. This pilot study highlighted the potential of these two detection methods (pharmacovigilance screening tool and analytical detection tool using the PharmaChk device). However, further research on a larger scale of samples and other therapeutic areas is required to validate these findings. This thesis highlights the need and importance of collaborations in identifying SSMs. Without the partnership between academia, industry and private laboratory institutions this research may have not been possible. The complex issue of SSMs requires this kind of engagement to enhance safe and effective medical treatment by decreasing the number of circulating SSMs worldwide

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe

Designing Data Spaces

This open access book provides a comprehensive view on data ecosystems and platform economics from methodical and technological foundations up to reports from practical implementations and applications in various industries. To this end, the book is structured in four parts: Part I “Foundations and Contexts” provides a general overview about building, running, and governing data spaces and an introduction to the IDS and GAIA-X projects. Part II “Data Space Technologies” subsequently details various implementation aspects of IDS and GAIA-X, including eg data usage control, the usage of blockchain technologies, or semantic data integration and interoperability. Next, Part III describes various “Use Cases and Data Ecosystems” from various application areas such as agriculture, healthcare, industry, energy, and mobility. Part IV eventually offers an overview of several “Solutions and Applications”, eg including products and experiences from companies like Google, SAP, Huawei, T-Systems, Innopay and many more. Overall, the book provides professionals in industry with an encompassing overview of the technological and economic aspects of data spaces, based on the International Data Spaces and Gaia-X initiatives. It presents implementations and business cases and gives an outlook to future developments. In doing so, it aims at proliferating the vision of a social data market economy based on data spaces which embrace trust and data sovereignty

Security Risk Management for the Internet of Things

In recent years, the rising complexity of Internet of Things (IoT) systems has increased their potential vulnerabilities and introduced new cybersecurity challenges. In this context, state of the art methods and technologies for security risk assessment have prominent limitations when it comes to large scale, cyber-physical and interconnected IoT systems. Risk assessments for modern IoT systems must be frequent, dynamic and driven by knowledge about both cyber and physical assets. Furthermore, they should be more proactive, more automated, and able to leverage information shared across IoT value chains. This book introduces a set of novel risk assessment techniques and their role in the IoT Security risk management process. Specifically, it presents architectures and platforms for end-to-end security, including their implementation based on the edge/fog computing paradigm. It also highlights machine learning techniques that boost the automation and proactiveness of IoT security risk assessments. Furthermore, blockchain solutions for open and transparent sharing of IoT security information across the supply chain are introduced. Frameworks for privacy awareness, along with technical measures that enable privacy risk assessment and boost GDPR compliance are also presented. Likewise, the book illustrates novel solutions for security certification of IoT systems, along with techniques for IoT security interoperability. In the coming years, IoT security will be a challenging, yet very exciting journey for IoT stakeholders, including security experts, consultants, security research organizations and IoT solution providers. The book provides knowledge and insights about where we stand on this journey. It also attempts to develop a vision for the future and to help readers start their IoT Security efforts on the right foot

The Internet of Everything

In the era before IoT, the world wide web, internet, web 2.0 and social media made people’s lives comfortable by providing web services and enabling access personal data irrespective of their location. Further, to save time and improve efficiency, there is a need for machine to machine communication, automation, smart computing and ubiquitous access to personal devices. This need gave birth to the phenomenon of Internet of Things (IoT) and further to the concept of Internet of Everything (IoE)