MNSSp3: Medical big data privacy protection platform based on Internet of things

A Secure High-Order Gene Interaction Detection Algorithm Based on Deep Neural Network

Identifying high-order Single Nucleotide Polymorphism (SNP) interactions of additive genetic model is crucial for detecting complex disease gene-type and predicting pathogenic genes of various disorders. We present a novel framework for high-order gene interactions detection, not directly identifying individual site, but based on Deep Learning (DL) method with Differential Privacy (DP), termed as Deep-DPGI. Firstly, integrate loss functions including cross-entropy and focal loss function to train the model parameters that minimize the value of loss. Secondly, use the layer-wise relevance analysis method to measure relevance difference between neurons weight and outputting results. Deep-DPGI disturbs neuron weight by adaptive noising mechanism, protecting the safety of high-order gene interactions and balancing the privacy and utility. Specifically, more noise is added to gradients of neurons that is less relevance with the outputs, less noise to gradients that more relevance. Finally, Experiments on simulated and real datasets demonstrate that Deep-DPGI not only improve the power of high-order gene interactions detection in with marginal and without marginal effect of complex disease models, but also prevent the disclosure of sensitive information effectively.

A comprehensive and systematic literature review on the big data management techniques in the internet of things

The Internet of Things (IoT) is a communication paradigm and a collection of heterogeneous interconnected devices. It produces large-scale distributed, and diverse data called big data. Big Data Management (BDM) in IoT is used for knowledge discovery and intelligent decision-making and is one of the most significant research challenges today. There are several mechanisms and technologies for BDM in IoT. This paper aims to study the important mechanisms in this area systematically. This paper studies articles published between 2016 and August 2022. Initially, 751 articles were identified, but a paper selection process reduced the number of articles to 110 significant studies. Four categories to study BDM mechanisms in IoT include BDM processes, BDM architectures/frameworks, quality attributes, and big data analytics types. Also, this paper represents a detailed comparison of the mechanisms in each category. Finally, the development challenges and open issues of BDM in IoT are discussed. As a result, predictive analysis and classification methods are used in many articles. On the other hand, some quality attributes such as confidentiality, accessibility, and sustainability are less considered. Also, none of the articles use key-value databases for data storage. This study can help researchers develop more effective BDM in IoT methods in a complex environment.

Protecting Sensitive Data in the Information Age: State of the Art and Future Prospects

The present information age is characterized by an ever-increasing digitalization. Smart devices quantify our entire lives. These collected data provide the foundation for data-driven services called smart services. They are able to adapt to a given context and thus tailor their functionalities to the user’s needs. It is therefore not surprising that their main resource, namely data, is nowadays a valuable commodity that can also be traded. However, this trend does not only have positive sides, as the gathered data reveal a lot of information about various data subjects. To prevent uncontrolled insights into private or confidential matters, data protection laws restrict the processing of sensitive data. One key factor in this regard is user-friendly privacy mechanisms. In this paper, we therefore assess current state-of-the-art privacy mechanisms. To this end, we initially identify forms of data processing applied by smart services. We then discuss privacy mechanisms suited for these use cases. Our findings reveal that current state-of-the-art privacy mechanisms provide good protection in principle, but there is no compelling one-size-fits-all privacy approach. This leads to further questions regarding the practicality of these mechanisms, which we present in the form of seven thought-provoking propositions.

The Effects of Service Quality of Medical Information O2O Platform on Continuous Use Intention: Case of South Korea

Digital transformation of the healthcare industry is being accelerated due to the evolution of digital intelligence information technology such as artificial intelligence (AI), Internet of Things (IoT), and big data. As online-to-offline (O2O)-based consumption life, based on platforms, becomes routinized along with the COVID-19 pandemic, the O2O platforms on medical activities are gaining attention. This study targeted the medical information O2O platform users and aimed to verify the effects of service quality factors on the platform users’ continuous use intention with the mediation of perceived usefulness and perceived convenience. Based on previous studies, four such factors: context-based affordability, immediacy of connection, reliability, and safety were defined as the medical information O2O platform service quality components. This study targeted 369 users of medical information O2O platforms with market dominance in Korea and conducted a questionnaire survey. According to analysis results, context-based affordability and immediacy of connection had a positive (+) effect on perceived usefulness and convenience, and they were confirmed to affect continuous-use intention with the mediation of the perceived usefulness and convenience. Meanwhile, reliability did not affect the perceived usefulness and convenience, whereas safety had a positive (+) effect on perceived usefulness but did not have the same effect (+) on perceived convenience. Consequently, it was ascertained that context-based affordability and immediacy of connection are more important factors to the medical information O2O platform consumers than reliability and safety.