Deep-Confidentiality : An IoT-Enabled Privacy-Preserving Framework for Unstructured Big Biomedical Data

Due to the Internet of Things evolution, the clinical data is exponentially growing and using smart technologies. The generated big biomedical data is confidential, as it contains a patient’s personal information and findings. Usually, big biomedical data is stored over the cloud, making it convenient to be accessed and shared. In this view, the data shared for research purposes helps to reveal useful and unexposed aspects. Unfortunately, sharing of such sensitive data also leads to certain privacy threats. Generally, the clinical data is available in textual format (e.g., perception reports). Under the domain of natural language processing, many research studies have been published to mitigate the privacy breaches in textual clinical data. However, there are still limitations and shortcomings in the current studies that are inevitable to be addressed. In this article, a novel framework for textual medical data privacy has been proposed as Deep-Confidentiality . The proposed framework improves Medical Entity Recognition (MER) using deep neural networks and sanitization compared to the current state-of-the-art techniques. Moreover, the new and generic utility metric is also proposed, which overcomes the shortcomings of the existing utility metric. It provides the true representation of sanitized documents as compared to the original documents. To check our proposed framework’s effectiveness, it is evaluated on the i2b2-2010 NLP challenge dataset, which is considered one of the complex medical data for MER. The proposed framework improves the MER with 7.8% recall, 7% precision, and 3.8% F1-score compared to the existing deep learning models. It also improved the data utility of sanitized documents up to 13.79%, where the value of the k is 3.

PANOLA: A Personal Assistant for Supporting Users in Preserving Privacy

Privacy is the right of individuals to keep personal information to themselves. When individuals use online systems, they should be given the right to decide what information they would like to share and what to keep private. When a piece of information pertains only to a single individual, preserving privacy is possible by providing the right access options to the user. However, when a piece of information pertains to multiple individuals, such as a picture of a group of friends or a collaboratively edited document, deciding how to share this information and with whom is challenging. The problem becomes more difficult when the individuals who are affected by the information have different, possibly conflicting privacy constraints. Resolving this problem requires a mechanism that takes into account the relevant individuals’ concerns to decide on the privacy configuration of information. Because these decisions need to be made frequently (i.e., per each piece of shared content), the mechanism should be automated. This article presents a personal assistant to help end-users with managing the privacy of their content. When some content that belongs to multiple users is about to be shared, the personal assistants of the users employ an auction-based privacy mechanism to regulate the privacy of the content. To do so, each personal assistant learns the preferences of its user over time and produces bids accordingly. Our proposed personal assistant is capable of assisting users with different personas and thus ensures that people benefit from it as they need it. Our evaluations over multiagent simulations with online social network content show that our proposed personal assistant enables privacy-respecting content sharing.

An assessment of cybersecurity awareness level among Northeastern University students in Nigeria

<p>The world economy today has adopted the internet as a medium of transactions, this has made many organizations use the internet for their daily activities. With this, there is an urgent need to have knowledge in cybersecurity and also how to defend critical assets. The objective of this paper is to identify the level of cybersecurity awareness of students in Northeastern Nigeria. A quantitative approach was used for data collection and cyberbully, personal information, internet banking, internet addiction, and Self-protection were the items ask for cybersecurity awareness level identification. Descriptive analysis was performed for initial result findings using SPSS and OriginPro for graphical design. the preliminary result shows of the students have some basic knowledge of cybersecurity in an item like internet banking, while other items like cyberbully, self-protection and, internet addiction result show moderate awareness, the students' participation based on gender, males constitute 77.1% i.e. (N=340) and females constitute 22.9% i.e. (N=101). Future research would concentrate on designing awareness programs that would increase the level of their awareness especially the students in the Northeastern part of Nigeria.</p>

xamination of Self-Control in Positive Psychotherapy Structures

Every day, individuals can encounter events which cause them to check their wishes and impulses. They need to provide self-control in the face of these events. It is observed that psychotherapies aimed at increasing self-control are limited. Positive psychotherapy, which is a structural and analytical psychotherapeutic method, can expand our viewpoint on this subject. Structures in positive psychotherapy were examined in the context of using the balance model, coupled with the ability of self-control. The dependent variable of the research is self-control, the independent variable is positive psychotherapy structures. Of the 151 (52.6%) of the participants (52.6%) were women, 136 (47.4%) were men. The Personal Information Form, which was created by the researcher as a data collection tool, the self-control scales and Wiesbaden positive psychotherapy and family therapy inventory were used. Multiple regression analysis was performed during the analysis of the data. As a result of multiple regression analysis, primary abilities (r = .51, r2 = .26; f = 11.840; p <.01), secondary abilities (r = .52, r2 = .27; f = 9.209; p <.01) and the balance model (r = .39, R2 = .15; f = 11.964; p <.01) significantly announced the self-control. According to the results of the analysis, patience, relationship, hope, and love are among the primary abilities; the secondary abilities are honesty, achievement, conformity and fairness. From the balance model, it was revealed that success and body were a significant predictor of self-control.

An explainable assistant for multiuser privacy

Multiuser Privacy (MP) concerns the protection of personal information in situations where such information is co-owned by multiple users. MP is particularly problematic in collaborative platforms such as online social networks (OSN). In fact, too often OSN users experience privacy violations due to conflicts generated by other users sharing content that involves them without their permission. Previous studies show that in most cases MP conflicts could be avoided, and are mainly due to the difficulty for the uploader to select appropriate sharing policies. For this reason, we present ELVIRA, the first fully explainable personal assistant that collaborates with other ELVIRA agents to identify the optimal sharing policy for a collectively owned content. An extensive evaluation of this agent through software simulations and two user studies suggests that ELVIRA, thanks to its properties of being role-agnostic, adaptive, explainable and both utility- and value-driven, would be more successful at supporting MP than other approaches presented in the literature in terms of (i) trade-off between generated utility and promotion of moral values, and (ii) users’ satisfaction of the explained recommended output.

The Current Status and Future Direction of Clinical Research in Japan From a Regulatory Perspective

In Japan, a law called the Clinical Trials Act went into being effective on April 1, 2018, and clinical research on human subjects conducted in Japan has been undergone major changes. Those other than clinical trials for marketing approval of drugs or medical devices are broadly classified into “specific clinical trials” and others, and regulations have been tightened for each. As a result, clinical interventional study was drastically reduced, and observational clinical study increased. For the observational clinical study, the two previous ethical guidelines were merged into the “Ethical Guidelines for Medical and Biological Research Involving Human Subjects,” which was enacted in March 2021. The observational clinical study is now subjected to these ethical guidelines. In addition, changes are planned for the Act on the Protection of Personal Information, which greatly affects data collection in clinical research. Clinical research in Japan must be conducted appropriately while adapting to these various changes in the external environment and legal framework. Adapting to these changes is not an easy task, as it requires increased financial and human resources for all stakeholders.

Configuration Method of AWS Security Architecture That Is Applicable to the Cloud Lifecycle for Sustainable Social Network

Recently, due to the many features and advantages of cloud computing, “cloud service” is being introduced to countless industries around the world at an unbelievably rapid pace. However, with the rapid increase in the introduction of cloud computing services, security vulnerabilities are increasing and the risk of technology leakage from cloud computing services is also expected to increase in social network service. Therefore, this study will propose an AWS-based (Amazon Web Services) security architecture configuration method that can be applied for the entire life cycle (planning, establishment, and operation) of cloud services for better security in AWS Cloud Services, which is the most used cloud service in the world. The proposed AWS security guide consists of five different areas, Security Solution Selection Guide, Personal Information Safeguard Guide, Security Architecture Design Guide, Security Configuration Guide, and Operational Security Checklist, for a safe social network. The AWS Security Architecture has been designed with three reference models: Standard Security Architecture, Basic Security Architecture, and Essential Security Architecture. The AWS Security Guide and AWS Security Architecture proposed in this paper are expected to help many businesses and institutions that are hoping to establish and operate a safe and reliable AWS cloud system in the social network environment.

Using deep learning to predict ideology from facial photographs: Expressions, beauty, and extra-facial information

Deep learning techniques can use common public data such as facial photographs to predict sensitive personal information, but little is known about what information contributes to the predictive success of these techniques. This lack of knowledge limits both the public’s ability to protect against revealing unintended information as well as the scientific utility of deep learning results. We combine convolutional neural networks, heat maps, facial expression coding, and classification of identifiable features such as masculinity and attractiveness in our study of political ideology in 3323 Danes. Predictive accuracy from the neural network was 61% in each gender. Model-predicted ideology correlated with aspects of both facial expressions (happiness vs neutrality) and morphology (specifically, attractiveness in females). Heat maps highlighted the informativeness of areas both on and off the face, pointing to methodological refinements and the need for future research to better understand the significance of certain facial areas.

Automatic Curation of Court Documents: Anonymizing Personal Data

In order to provide open access to data of public interest, it is often necessary to perform several data curation processes. In some cases, such as biological databases, curation involves quality control to ensure reliable experimental support for biological sequence data. In others, such as medical records or judicial files, publication must not interfere with the right to privacy of the persons involved. There are also interventions in the published data with the aim of generating metadata that enable a better experience of querying and navigation. In all cases, the curation process constitutes a bottleneck that slows down general access to the data, so it is of great interest to have automatic or semi-automatic curation processes. In this paper, we present a solution aimed at the automatic curation of our National Jurisprudence Database, with special focus on the process of the anonymization of personal information. The anonymization process aims to hide the names of the participants involved in a lawsuit without losing the meaning of the narrative of facts. In order to achieve this goal, we need, not only to recognize person names but also resolve co-references in order to assign the same label to all mentions of the same person. Our corpus has significant differences in the spelling of person names, so it was clear from the beginning that pre-existing tools would not be able to reach a good performance. The challenge was to find a good way of injecting specialized knowledge about person names syntax while taking profit of previous capabilities of pre-trained tools. We fine-tuned an NER analyzer and we built a clusterization algorithm to solve co-references between named entities. We present our first results, which, for both tasks, are promising: We obtained a 90.21% of F1-micro in the NER task—from a 39.99% score before retraining the same analyzer in our corpus—and a 95.95% ARI score in clustering for co-reference resolution.