Precision health data: Requirements, challenges and existing techniques for data security and privacy

Federated Learning for Electronic Health Records

In data-driven medical research, multi-center studies have long been preferred over single-center ones due to a single institute sometimes not having enough data to obtain sufficient statistical power for certain hypothesis testings as well as predictive and subgroup studies. The wide adoption of electronic health records (EHRs) has made multi-institutional collaboration much more feasible. However, concerns over infrastructures, regulations, privacy and data standardization present a challenge to data sharing across healthcare institutions. Federated Learning (FL), which allows multiple sites to collaboratively train a global model without directly sharing data, has become a promising paradigm to break the data isolation. In this study, we surveyed existing works on FL applications in EHRs and evaluated the performance of current state-of-the-art FL algorithms on two EHR machine learning tasks of significant clinical importance on a real world multi-center EHR dataset.

An Improved COVID-19 Detection using GAN-Based Data Augmentation and Novel QuNet-Based Classification

COVID-19 is a fatal disease caused by the SARS-CoV-2 virus that has caused around 5.3 Million deaths globally as of December 2021. The detection of this disease is a time taking process that have worsen the situation around the globe, and the disease has been identified as a world pandemic by the WHO. Deep learning-based approaches are being widely used to diagnose the COVID-19 cases, but the limitation of immensity in the publicly available dataset causes the problem of model over-fitting. Modern artificial intelligence-based techniques can be used to increase the dataset to avoid from the over-fitting problem. This research work presents the use of various deep learning models along with the state-of-the-art augmentation methods, namely, classical and generative adversarial network- (GAN-) based data augmentation. Furthermore, four existing deep convolutional networks, namely, DenseNet-121, InceptionV3, Xception, and ResNet101 have been used for the detection of the virus in X-ray images after training on augmented dataset. Additionally, we have also proposed a novel convolutional neural network (QuNet) to improve the COVID-19 detection. The comparative analysis of achieved results reflects that both QuNet and Xception achieved high accuracy with classical augmented dataset, whereas QuNet has also outperformed and delivered 90% detection accuracy with GAN-based augmented dataset.

Achieving data privacy for decision support systems in times of massive data sharing

The world is suffering from a new pandemic of Covid-19 that is affecting human lives. The collection of records for Covid-19 patients is necessary to tackle that situation. The decision support systems (DSS) are used to gather that records. The researchers access the patient's data through DSS and perform predictions on the severity and effect of the Covid-19 disease; in contrast, unauthorized users can also access the data for malicious purposes. For that reason, it is a challenging task to protect Covid-19 patient data. In this paper, we proposed a new technique for protecting Covid-19 patients' data. The proposed model consists of two folds. Firstly, Blowfish encryption uses to encrypt the identity attributes. Secondly, it uses Pseudonymization to mask identity and quasi-attributes, then all the data links with one another, such as the encrypted, masked, sensitive, and non-sensitive attributes. In this way, the data becomes more secure from unauthorized access.

Towards Design and Development of a Data Security and Privacy Risk Management Framework for WBAN Based Healthcare Applications

Assuring security and privacy of data is a key challenge for organizations when developing WBAN applications. The reasons for this challenge include (i) developers have limited knowledge of market-specific regulatory requirements and security standards, and (ii) there are a vast number of security controls with insufficient implementation detail. To address these challenges, we have developed a WBAN data security and privacy risk management framework. The goal of this paper is trifold. First, we present the methodology used to develop the framework. The framework was developed by considering recommendations from legislation and standards. Second, we present the findings from an initial validation of the framework’s usability and effectiveness of the security and privacy controls. Finally, we present an updated version of the framework and explain how it addresses the aforementioned challenges.

Privacy-preserving data sharing infrastructures for medical research: systematization and comparison

BACKGROUND

Data sharing is considered a crucial part of modern medical research. Unfortunately, despite its advantages, it often faces obstacles, especially data privacy challenges. As a result, various approaches and infrastructures have been developed that aim to ensure that patients and research participants remain anonymous when data is shared. However, privacy protection typically comes at a cost, e.g. restrictions regarding the types of analyses that can be performed on shared data. What is lacking is a systematization making the trade-offs taken by different approaches transparent. The aim of the work described in this paper was to develop a systematization for the degree of privacy protection provided and the trade-offs taken by different data sharing methods. Based on this contribution, we categorized popular data sharing approaches and identified research gaps by analyzing combinations of promising properties and features that are not yet supported by existing approaches.

METHODS

The systematization consists of different axes. Three axes relate to privacy protection aspects and were adopted from the popular Five Safes Framework: (1) safe data, addressing privacy at the input level, (2) safe settings, addressing privacy during shared processing, and (3) safe outputs, addressing privacy protection of analysis results. Three additional axes address the usefulness of approaches: (4) support for de-duplication, to enable the reconciliation of data belonging to the same individuals, (5) flexibility, to be able to adapt to different data analysis requirements, and (6) scalability, to maintain performance with increasing complexity of shared data or common analysis processes.

RESULTS

Using the systematization, we identified three different categories of approaches: distributed data analyses, which exchange anonymous aggregated data, secure multi-party computation protocols, which exchange encrypted data, and data enclaves, which store pooled individual-level data in secure environments for access for analysis purposes. We identified important research gaps, including a lack of approaches enabling the de-duplication of horizontally distributed data or providing a high degree of flexibility.

CONCLUSIONS

There are fundamental differences between different data sharing approaches and several gaps in their functionality that may be interesting to investigate in future work. Our systematization can make the properties of privacy-preserving data sharing infrastructures more transparent and support decision makers and regulatory authorities with a better understanding of the trade-offs taken.

Quantitative Assessment of Cybersecurity Risks for Mitigating Data Breaches in Business Systems

The evaluation of data breaches and cybersecurity risks has not yet been formally addressed in modern business systems. There has been a tremendous increase in the generation, usage and consumption of industrial and business data as a result of smart and computational intensive software systems. This has resulted in an increase in the attack surface of these cyber systems. Consequently, there has been a consequent increase in the associated cybersecurity risks. However, no significant studies have been conducted that examine, compare, and evaluate the approaches used by the risk calculators to investigate the data breaches. The development of an efficient cybersecurity solution allows us to mitigate the data breaches threatened by the cybersecurity risks such as cyber-attacks against database storage, processing and management. In this paper, we develop a comprehensive, formal model that estimates the two components of security risks: breach cost and the likelihood of a data breach within 12 months. The data used in this model are taken from the industrial business report, which provides the necessary information collected and the calculators developed by the major organizations in the field. This model integrated with the cybersecurity solution uses consolidated factors that have a significant impact on the data breach risk. We propose mathematical models of how the factors impact the cost and the likelihood. These models allow us to conclude that results obtained through the models mitigate the data breaches in the potential and future business system dynamically.