Data Privacy Protection in News Crowdfunding in the Era of Artificial Intelligence

This paper aims to study the protection of data privacy in news crowdfunding in the era of artificial intelligence. This paper respectively quotes the encryption algorithm of artificial intelligence data protection and the BP neural network prediction model to analyze the data privacy protection in news crowdfunding in the artificial intelligence era. Finally, this paper also combines the questionnaire survey method to understand the public’s awareness of privacy. The results of this paper show that artificial intelligence can promote personal data awareness and privacy, improve personal data and privacy measures and methods, and improve the effectiveness and level of privacy and privacy. In the analysis, the survey found that male college students only have 81.1% of the cognition of personal trait information, only 78.5% of network trace information, and only 78.3% of female college students’ cognition of personal credit.

Highly Secured Dynamic Color QR Pattern Generation for Real Time Application

This work advances the state-of-art secured WBAN system and QR pattern enabled authentication for privacy measures. An attempt was made to integrate all the above process to build high performance WBAN system. In this work, a comprehensive statistical framework is developed with randomized key generation and secured cipher transformation for secured sensor node communication. We create primary colour channels based on three different QR codes that are widely used for colour printing and complementary channels for capturing colour images. Last but not least, we produced a colour QR pattern.

Different Methods of Encryption and Decryption

Data is any type of stored digital information. Security is about the protection of assets. Data security refers to protective digital privacy measures that are applied to prevent unauthorized access to computers, personal databases and websites. Cryptography is evergreen and developments. Cryptography protects users by providing functionality for the encryption of data and authentication of other users. Compression is the process of reducing the number of bits or bytes needed to represent a given set of data. It allows saving more data. Cryptography is a popular ways of sending vital information in a secret way. There are many cryptographic techniques available and among them AES is one of the most powerful techniques. The scenario of present day of information security system includes confidentiality, authenticity, integrity, nonrepudiation. The security of communication is a crucial issue on World Wide Web. It is about confidentiality, integrity, authentication during access or editing of confidential internal documents. Keywords: Cryptography, Hill Cipher, Homophonic Substitution Cipher, Monoalphabetic Cipher, Ceaser Cipher.

Vaginal examinations and mistreatment of women during facility-based childbirth in health facilities: secondary analysis of labour observations in Ghana, Guinea and Nigeria

BackgroundPrevious research on mistreatment of women during childbirth has focused on physical and verbal abuse, neglect and stigmatisation. However, other manifestations of mistreatment, such as during vaginal examinations, are relatively underexplored. This study explores four types of mistreatment of women during vaginal examinations: (1) non-consented care, (2) sharing of private information, (3) exposure of genitalia and (4) exposure of breasts.MethodsA secondary analysis of data from the WHO multicountry study ‘How Women Are Treated During Childbirth’ was conducted. The study used direct, continuous labour observations of women giving birth in facilities in Ghana, Guinea and Nigeria. Descriptive and multivariable logistic regression analyses were used to describe the different types of mistreatment of women during vaginal examinations and associated privacy measures (ie, availability of curtains).ResultsOf the 2016 women observed, 1430 (70.9%) underwent any vaginal examination. Across all vaginal examinations, 842/1430 (58.9%) women were observed to receive non-consented care; 233/1430 (16.4%) women had their private information shared; 397/1430 (27.8%) women had their genitalia exposed; and 356/1430 (24.9%) had their breasts exposed. The observed prevalence of mistreatment during vaginal examinations varied across countries. There were country-level differences in the association between absence of privacy measures and mistreatment. Absence of privacy measures was associated with sharing of private information (Ghana: adjusted OR (AOR) 3.8, 95% CI 1.6 to 8.9; Nigeria: AOR 4.9, 95% CI 1.9 to 12.7), genitalia exposure (Ghana: AOR 6.7, 95% CI 2.9 to 14.9; Nigeria: AOR 6.5, 95% CI 2.9 to 14.5), breast exposure (Ghana: AOR 5.9, 95% CI 2.8 to 12.9; Nigeria: AOR 2.7, 95% CI 1.3 to 5.9) and non-consented vaginal examination (Ghana: AOR 2.5, 95% CI 1.4 to 4.7; Guinea: AOR 0.21, 95% CI 0.12 to 0.38).ConclusionOur results highlight the need to ensure better communication and consent processes for vaginal examination during childbirth. In some settings, measures such as availability of curtains were helpful to reduce women’s exposure and sharing of private information, but context-specific interventions will be required to achieve respectful maternity care globally.

Privacy preserving defect prediction using generalization and entropy-based data reduction

The software engineering community produces data that can be analyzed to enhance the quality of future software products, and data regarding software defects can be used by data scientists to create defect predictors. However, sharing such data raises privacy concerns, since sensitive software features are usually considered as business assets that should be protected in accordance with the law. Early research efforts on protecting the privacy of software data found that applying conventional data anonymization to mask sensitive attributes of software features degrades the quality of the shared data. In addition, data produced by such approaches is not immune to attacks such as inference and background knowledge attacks. This research proposes a new approach to share protected release of software defects data that can still be used in data science algorithms. We created a generalization (clustering)-based approach to anonymize sensitive software attributes. Tomek link and AllNN data reduction approaches were used to discard noisy records that may affect the usefulness of the shared data. The proposed approach considers diversity of sensitive attributes as an important factor to avoid inference and background knowledge attacks on the anonymized data, therefore data discarded is removed from both defective and non-defective records. We conducted experiments conducted on several benchmark software defect datasets, using both data quality and privacy measures to evaluate the proposed approach. Our findings showed that the proposed approach outperforms existing well-known techniques using accuracy and privacy measures.

Multipurpose Mobile Apps for Mental Health in Chinese App Stores: Content Analysis and Quality Evaluation (Preprint)

BACKGROUND Mental disorders impose varying degrees of burden on patients and their surroundings. However, people are reluctant to take the initiative to seek mental health services because of the uneven distribution of resources and stigmatization. Thus, mobile apps are considered an effective way to eliminate these obstacles and improve mental health awareness. OBJECTIVE This study aimed to evaluate the quality, function, privacy measures, and evidence-based and professional background of multipurpose mental health apps in Chinese commercial app stores. METHODS A systematic search was conducted on iOS and Android platforms in China to identify multipurpose mental health apps. Two independent reviewers evaluated the identified mobile apps using Mobile App Rating Scale (MARS). Each app was downloaded, and the general characteristics, privacy and security measures, development background, and functional characteristics of each app were evaluated. RESULTS A total of 40 apps were analyzed, of which 35 apps (87.5%) were developed by companies and 33 apps (82.5%) provided links to access the privacy policy; 52.5% did not mention the involvement of relevant professionals or the guidance of scientific basis in the app development process. The main built-in functions of these apps include psychological education (38/40, 95%), self-assessment (34/40, 85%), and counseling (33/40, 83%). The overall quality average MARS score of the 40 apps was 3.53 (standard deviation 0.39), and the total score was between 2.96 and 4.30. The total score of MARS was significantly positively correlated with the scores of each subscale (r = 0.62–0.88; P <.001). However, the user score of the app market was not significantly correlated with the total score of MARS (r = 0.23; P =.19). CONCLUSIONS The quality of multipurpose mental health apps in China’s main app market is generally good and provides various functional combinations. However, health professionals are less involved in the development of these apps, and the privacy protection policy of the apps also needs to be described in more detail. This study provides a reference for the development of multipurpose mental health apps.

Perceived Risk of Re-identification in OMOP-CDM Database: A Cross-Sectional Survey (Preprint)

BACKGROUND The advancement of information technology has immensely increased the quality and volume of health data. This has led to an increase in observational study, as well as to the threat of privacy invasion. Recently, a distributed research network based on the common data model (CDM) has emerged, enabling collaborative international medical research without sharing patient-level data. Although the CDM database for each institution is built inside a firewall, the risk of re-identification requires management. OBJECTIVE This study aims to elucidate the perceptions CDM users have towards CDM and risk management for re-identification. METHODS The survey, targeted to answer specific in-depth questions on CDM, was conducted from October - November 2020. We targeted well-experienced researchers who actively use CDM. Basic statistics (total number and percent) were computed for all covariates. RESULTS There were 33 valid respondents. Of these, 43.8% demonstrated supplementary privacy measures were unnecessary, as the “minimum cell count” parameter was effective in minimizing the liability of re-identification. During extract-transform-load processes, 81.8% of respondents assumed structured data is under control from the risk of re-identification. However, respondents noted that date of birth and death were highly re-identifiable information. The majority of respondents (n=22, 66.7%) conceded the possibility of identifier-contained unstructured data in the NOTE table. CONCLUSIONS Overall, CDM users generally attributed high reliability for privacy protection to the intrinsic nature of CDM. There was little demand for additional de-identification methods. However, unstructured data in the CDM were suspected to have risks. The necessity for a coordinating consortium to define and manage the re-identification risk of CDM was urged.

Exploring potential gender differences in information security and privacy

Purpose Information technology users often fail to adopt necessary security and privacy measures, leading to increased risk of cybercrimes. There has been limited research on how demographic differences influence information security behaviour and understanding this could be important in identifying users who may be more likely to have poor information security behaviour. This study aims to investigate whether there are any gender differences in security and privacy behaviours and perceptions, to identify potential differences that may have implications for protecting users’ privacy and securing their devices, software and data. Design/methodology/approach This paper addresses this research gap by investigating security behaviours and perceptions in the following two studies: one focussing on information security and one on information privacy. Data was collected in both studies using anonymous online surveys. Findings This study finds significant differences between men and women in over 40% of the security and privacy behaviours considered, suggesting that overall levels of both are significantly lower for women than for men, with behaviours that require more technical skill being adopted less by female users. Furthermore, individual perceptions exhibited some gender differences. Originality/value This research suggests that potential gender differences in some security and privacy behaviours and perceptions should be taken into account when designing information security education, training and awareness initiatives for both organisations and the broader community. This study also provides a strong foundation to explore information security individual differences more deeply.

High national acceptance for COVID-19 contact tracing technologies in Taiwan (Preprint)

BACKGROUND Governments worldwide have introduced COVID-19 tracing technologies. Taiwan, a world leader in controlling the virus’ spread, has introduced the Taiwan ‘Social Distancing App’ to facilitate COVID-19 contact tracing. However, for these technologies to be effective, they must be accepted and used by the public. OBJECTIVE Our study aimed to determine public acceptance for three hypothetical tracing technologies: a centralized Government App, a decentralized Bluetooth App (e.g., Taiwan’s Social Distancing App), and a Telecommunication tracing technology; and model what factors contributed to their acceptance. METHODS Four nationally representative surveys were conducted in April 2020 sampling 6,000 Taiwanese residents. Perceptions and impacts of COVID-19, government effectiveness, worldviews, and attitudes towards and acceptance of one-of-three hypothetical tracing technologies were assessed. RESULTS Technology acceptance was high across all hypothetical technologies (67% - 73%) and improved with additional privacy measures (82% - 88%). Bayesian modelling (using 95% highest density credible intervals) showed data sensitivity and perceived poor COVID-19 policy compliance inhibited technology acceptance. By contrast, technology benefits (e.g., returning to activities, reducing virus spread, lowering the likelihood of infection), higher education, and perceived technology privacy, security, and trust, were all contributing factors to overall acceptance. Bayesian ordinal probit models revealed higher COVID-19 concern for other people than for one’s self. CONCLUSIONS Taiwan is currently using a range of technologies to minimize the spread of COVID-19 as the country returns to normal economic and social activities. We observed high acceptance for COVID-19 tracing technologies among the Taiwanese public, a promising and necessary finding for the successful introduction of Taiwan’s new ‘Social Distancing App’. Policy makers may capitalize on this acceptance by focusing attention towards the App’s benefits, privacy and security measures, making the App’s privacy measures transparent to the public, and emphasizing App uptake and compliance among the public. CLINICALTRIAL Not applicable.

High national acceptance for COVID-19 contact tracing technologies in Taiwan

Taiwan has been a world leader in controlling the spread of SARS-CoV-2 during the COVID-19 pandemic. Recently, the Taiwan Government launched its COVID-19 tracing App the `Taiwan Social Distancing App', however the effectiveness of this tracing App depends on its acceptance and uptake among the general population. We measured acceptance for three hypothetical tracing technologies (telecommunication network tracing, a government App, and the Apple and Google Bluetooth exposure notification system) in four nationally representative Taiwanese samples. Using Bayesian methods, we find high acceptance for all three tracking technologies, with acceptance increasing with the inclusion of additional privacy measures. Modelling revealed acceptance increased with the perceived technology benefits, trust in the providers' intent, data security and privacy measures, the level of ongoing control, and one's level of education. Acceptance decreased with data sensitivity perceptions, and perceived low policy compliance by others in the general public. We consider the policy implications of these results for Taiwan during the COVID-19 pandemic and into the future.