Privacy Of Personal Information Research Articles

A secure data fitting scheme based on CKKS homomorphic encryption for medical IoT

With the development of big data technology, medical data has become increasingly important. It not only contains personal privacy information, but also involves medical security issues. This paper proposes a secure data fitting scheme based on CKKS (Cheon-Kim-Kim-Song) homomorphic encryption algorithm for medical IoT. The scheme encrypts the KGGLE-HDP (Heart Disease Prediction) dataset through CKKS homomorphic encryption, calculates the data’s weight and deviation. By using the gradient descent method, it calculates the weight and bias of the data. The experimental results show that under the KAGGLE-HDP dataset,we select the threshold value is 0.7 and the parameter setting is (Poly_modulus_degree, Coeff_mod_bit_sizes, Scale) = (16384; 43, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 43; 23), the number of iteration is 3 and the recognition accuracy of this scheme can achieve 96.7%. The scheme shows that it has a high recognition accuracy and better privacy protection than other data fitting schemes.

On the Legal Protection of Campus Online Loans and Its Regulation

With the continuous development of campus online loans, many problems have emerged while bringing convenience to college students. Some campus online lending institutions have false propaganda and unfair competition, charging high and unreasonable fees when students take out loans, tempting students to “use loans to support loans”, buying and selling students’ personal privacy information, and using violent collection methods when students cannot repay the loans in time. In the face of these problems, the government should improve the supervision system of campus online lenders, establish a database to provide personal credit assessment, and schools should enhance the management of campus online lending, regularly carry out mapping work, and enhance the legal awareness of college students, while promoting the participation of governmental supervision departments, schools, and society, so that campus online lending can develop in a benign way and provide more convenience forcollege students.

Digital privacy of smartphone camera-based assistive technology for users with visual disabilities

Smartphone users with visual disabilities often use camera-based assistive technology applications such as Seeing AI, BeMyEyes, and TapTapSee apps in identifying people and objects. As personal information could be shared, digital privacy issues are critical to those with visual disabilities. However, little is known about user perspectives on digital privacy issues - e.g., the degree to which those with visual disabilities understand user privacy policies and the degree to which they intend to adopt those apps. To address the knowledge gap, this study conducted interviews with a convenience sample of 30 participants with visual disabilities. The results indicate that those with visual disabilities had a lack of knowledge about privacy policies and potential risks of privacy and security breaches of personal information. The results contributed to forming a conceptual framework that contains a list of facilitators and barriers to user adoption, which could serve as a knowledge foundation for many other researchers and professionals to support and enhance users' privacy awareness, accessible to users with visual disabilities.

Online national citizen's ID renewal system

Through the National Registration Bureau (NRB), the Malawi government introduced mandatory registration and the issuance of National Identity Cards (IDs) to its bona fide Malawian citizens aged 16 and above, in accordance with the Registration Act of 2010. This administration is currently carried out manually in a few designated National Registration Bureau (NRB) offices. As a result, citizens find it time-consuming and congested to renew their IDs in case of loss, damage, or expiration. Due to these drawbacks, there was a need for a new system that could help overcome these challenges, leading to the proposition of an "Online National Citizen's ID Renewal System. The system is aimed at simplifying the process of renewing national ID cards for Malawian citizens in a convenient and secure manner. Through the web portal provided by the system, citizens are able to complete the renewal process in one sitting without being required to physically visit the National Registration Bureau offices (NRB). The functionality of the system requires citizens to create an account, wherein their names and their previous ID numbers are used as usernames and passwords, respectively, or credentials to log in to the system. Once the information has been verified, citizens would be able to access renewal or registration forms (which are currently collected, filled, and submitted manually) and pay the relevant renewal fee, which is currently at MWK2500, through secure payment gateways. To ensure the security and privacy of the citizens' personal information, the system is implementing industry-standard encryption and authentication protocols and is complying with data protection laws and regulations mandated by the Malawi Government. The online national Citizens ID renewal system will provide a more efficient, accessible, and secure system for managing citizen ID renewals. It has also reduced waiting times, improved accuracy, and increased accessibility for all citizens, while also providing better security and efficiency for government staff managing the system. The Online National Citizen's ID Renewal System is expected to improve the current manual ID renewal system, which poses several challenges and limitations, such as long waiting times, errors, inaccuracies, and inefficiency.

View Analysis of Personal Information Leakage and Privacy Protection in Big Data Era—Based on Q Method

View Analysis of Personal Information Leakage and Privacy Protection in Big Data Era—Based on Q Method

Privacy preserving dynamic data release based on non-synonymous diverse anatomy

The publishing and using of big data brought unprecedented convenience to users. However, it also results in the disclosure of personal privacy information. In order to mitigate the privacy leakage risk of sensitive information during dynamic data updating, this paper envisages a non-synonymous diverse anatomy method for the privacy preserving dynamic data publishing. The envisaged method inherits the advantages of the traditional anatomy method, retains the availability of the original data to the greatest extent, and avoids the loss of information caused by the generalisation process. A series of indicators are designed to evaluate the synonymous linkage between non-numerical sensitive values. A novel grouping mechanism is further proposed to achieve l-diversity anatomy by combining the concept of synonymous linkage and synonymous entropy with the dynamic update procedure. Experimental analysis suggests that the envisaged method can provide better privacy protection effect on the published data.

Research on Personal Information Security Protection Measures in the Big Data Era

While big data brings convenience to people, it also exposes various problems. Personal information is always exposed to risks. Protecting personal information from infringement in the big data era has become an urgent problem to be solved. This paper studies from three aspects. First, it analyzes the importance of personal information security protection in the big dataera: maintaining normal social production and life order, protecting personal physical and mental health and property safety, and reducing the occurrence of cyber crimes. Second, it summarizes the problems existing in personal information security protection in the big data era: serious disclosure of personal privacy information, malicious attacks on personal privacy by cyber hackers, low awareness of personal information security protection, and imperfect laws and regulations on personal information security protection. Third, the main measures of personal information security protection in the big data eraare put forward: improving the laws and regulations of personal information protection, raising the awareness of personal information security protection, building a data-centric security governance system, and strengthening the prevention and governance of cyber hacking attacks.

A comparative study on HIPAA technical safeguards assessment of android mHealth applications

Protecting personal health records is becoming increasingly important as more people use Mobile Health applications (mHealth apps) to improve their health outcomes. These mHealth apps enable consumers to monitor their health-related problems, store, manage, and share health records, medical conditions, treatment, and medication. With the increase of mHealth apps accessibility and usability, it is crucial to create, receive, maintain or transmit protected health information (PHI) on behalf of a covered entity or another business associate. The Health Insurance Portability and Accountability Act (HIPAA) provides guidelines to the app developers so that the apps must be compliant with required and addressable Technical Safeguards. However, most mobile app developers, including mHealth apps are not aware of HIPAA security and privacy regulations. Therefore, a research opportunity has emerged to develop an analytical framework to assist the developer to maintain a secure and HIPAA-compliant source code and raise awareness among consumers about the privacy and security of sensitive and personal health information. We proposed an Android source code analysis framework that evaluates twelve HIPAA Technical Safeguards to check whether a mHealth application is HIPAA compliant or not. The implemented meta-analysis and data-flow analysis algorithms efficiently identify the risk and safety features of mHealth apps that violate HIPAA regulations. Furthermore, we addressed API level checking for secure data communication mandated by recent CMS guidelines between third-party mobile health apps and EHR systems. Experimentally, a web-based tool has been developed for evaluating the efficacy of analysis techniques and algorithms. We have investigated 200 top popular Medical and Health & Fitness category Android apps collected from Google Play Store. We identified from the comparative analysis of the HIPAA rules assessment results that authorization to access sensitive resources, data encryption–decryption, and data transmission security is the most vulnerable features of the investigated apps. We provided recommendations to app developers about the most common mistake made at the time of app development and how to avoid these mistakes to implement secure and HIPAA-compliant apps. The proposed framework enables us to develop an IDE plugin for mHealth app developers and a web-based interface for mHealth app consumers.

A Systematic Analysis of the Capital One Data Breach: Critical Lessons Learned

The 2019 Capital One data breach was one of the largest data breaches impacting the privacy and security of personal information of over a 100 million individuals. In most reports about a cyberattack, you will often hear that it succeeded because a single employee clicked on a link in a phishing email or forgot to patch some software, making it seem like an isolated, one-off, trivial problem involving maybe one person, committing a mistake or being negligent. But that is usually not the complete story. By ignoring the related managerial and organizational failures, you are leaving in place the conditions for the next breach. Using our Cybersafety analysis methodology, we identified control failures spanning control levels, going from rather technical issues up to top management, the Board of Directors, and Government regulators. In this analysis, we reconstruct the Capital One hierarchical cyber safety control structure, identify what parts failed and why, and provide recommendations for improvements. This work demonstrates how to discover the true causes of security failures in complex information systems and derive systematic cybersecurity improvements that likely apply to many other organizations. It also provides an approach that individuals can use to evaluate and better secure their organizations.

Plenary 2 Benefits and challenges of the European Health Data Space

Abstract The creation of a European Health Data Space (EHDS) is one of the key components of a strong European Health Union. The objectives of the EHDS are: i) Empower individuals through better digital access to their personal health data; support free movement by ensuring that health data follow people; ii) Unleash the data economy by fostering a genuine single market for digital health services and products; and iii) Set up strict rules for the use of individual’s non-identifiable health data for research, innovation, policy-making and regulatory activities. As such, the EHDS aims to improve and support healthcare delivery within Europe by allowing public health data to be accessible throughout Europe. The EHDS also aims to promote better access and exchange of different types of health data for research and policy purposes. The aim is to have the EHDS up and running in 2025. The EHDS is expected to bring great benefit, but it also brings challenges related to technology, governance and privacy. The exchange of data at European level means that health data from different sources need to be able to talk to each other. Making the data Findable, Accessible, Interoperable, and Re-usable is key to the success of the EHDS. Moreover, the diversity of Europe’s health information systems need to be taken in account. The EHDS will also have to be transparent to ensure privacy of personal information included in the EHDS. Speakers: Fulvia Raffaelli European Commission, Brussels, Belgium Petronille Bogaert Sciensano, Brussels, Belgium Irene Schlünder TMF, Germany Emmanuel Bacry Health Data Hub, France

Health Records Database and Inherent Security Concerns: A Review of the Literature.

The use of electronic health records (EHRs) has grown significantly in the past decade. Health information databases contain sensitive patient information, including their names and addresses, tests, diagnoses, treatment, and medical history. This information should be secured and protected from manipulation and fraudulent use by third parties. EHRs are expected to increase efficiency in healthcare delivery, improve healthcare quality, and relieve increased financial pressure. Despite these expected benefits, EHRs are potentially vulnerable to security concerns that may affect the confidentiality and privacy of patients' personal information. This paper presents a literature review of EHRs, factors that support the security and safety of health records, potential security breaches, and solutions to inherent security concerns. The study collects data through a systematic review of past studies that have addressed the topic of EHRs and security issues, and other relevant publications on EHR systems, and procedures that help safeguard health records databases. A total of 30 sources are analyzed for all pertinent information regarding security concerns of health records databases. These sources were obtained through an internet search on credible databases, including Google Scholar, PubMed, and CINAHL databases. The results of the current study reveal the perceived vulnerability of EHRs to security concerns, common security issues, the nature of these common security concerns, Health Insurance Portability and Accountability Act rules, provider responsibilities, and recommendations for reducing EHR security risks. This paper also reveals effective strategies such as privacy-protection awareness and staff training to enhance the security of health records databases.

The right to privacy and an implication of the EU General Data Protection Regulation (GDPR) in Europe: challenges to the companies

ABSTRACT The article traces the European Union (EU)’s General Data Protection Regulation (GDPR) and implication in the Europe. In the era of global digitalisation, the right to respect private life, communication and the home has become a matter of protection. Protecting the right to privacy is a responsibility of a state which includes privacy of personal information, e.g. birth, messages, phone call and number and emails. Likewise, this study explains EU concern’s about its citizens’ privacy and the recent inclusion of the GDPR for the protection of natural persons. The article aims to explore individual fundamental rights and implications in the digital age, as well as cooperation data rules between companies and public bodies. At the same time, questions arise about the rightful implication of GDPR and the right to privacy of the public through protection, especially from tech companies. For validating the argument, various qualitative research methods were applied. The COVID-19 pandemic has raised a serious question over privacy rights protection by the government, which supports our findings that EU GDPR has a long road to go and have challenges. Its credibility of lawful data activities is also a matter of concern and a reliable promise by the member states and the EU.

Техногенная цивилизация и новые формы неравенства

The author draws attention to the change in the social structure of society, the emer-gence of new social groups and forms of employment, which is characteristic of tech-nogenic civilization. Negative aspects of the use of modern technologies are noted (such as the possibility of total state control, a change in society's attitude to the pri-vacy of personal information, the transformation of economic and labor relations, the transformation of specialist skills, desocialization). Hidden negative aspects are pointed out – increasing inequality from legal, social, economic, moral – to anthropo-logical, biological and genetic, as well as the emergence of an even more dangerous and new form of inequality associated with access to the latest effective technologies and the commercialization of services based on biotechnology. The emergence of the precariat as one of the modern forms of labor inequality is investigated. The question is raised about the person's perception of the pace of socio-technological transition and the adaptation of this economic structure to a new reality.

Broken Heart: Privacy Leakage Analysis on ECG-Based Authentication Schemes

Authentications using biometrics, such as fingerprint recognition and electrocardiogram (ECG), have been actively used in various applications. Unlike traditional authentication methods, such as passwords or PINs, biometric-based authentication has an advantage in terms of security owing to its capability of liveness detection. Among the various types of biometrics, ECG-based authentication is widely utilized in many fields. Because of the inherent characteristics of ECG, however, the incautious design of ECG-based authentication may result in serious leakage of personal private information. In this paper, we extensively investigate ECG-based authentication schemes previously proposed in the literature and analyze possible privacy leakages by employing machine learning and deep learning techniques. We found that most schemes suffer from vulnerabilities that lead to the leakage of personal information, such as gender, age, and even diseases. We also identified some privacy-insensitive ECG fiducial points by utilizing feature selection algorithms. Based on these features, we present a privacy-preserving ECG-based authentication scheme.

Does privacy breach affect firm performance? An analysis incorporating event-induced changes and event clustering

Previous studies have analyzed the impact of cybersecurity breaches on firm performance, but the impact of the privacy breach on firm performance is less explored. Needless to say, the privacy of the individual's personal information has been a rising concern for organizations over the years. Previous studies, primarily focused on cybersecurity breaches, have used a simple market model (MM) to observe the impact of these breaches on firm performance. Our study has used an advanced market model (AMM), which observes the event-induced changes in the variance of stock returns and changes in MM parameters, which are ignored by the MM. The ignorance of this may lead to a biased cumulative abnormal return (CAR) computation. We have also included the event clustering observation (events that are very close to each other) in our study and evaluated AMM with seemingly unrelated regression (SUR), i.e., AMM-SUR. We have used data of 193 privacy breaches related to the US firms listed on NYSE (USA), NASDAQ (USA) for the years 2012 (43 breaches), 2013 (37 breaches), 2014 (51 breaches), and 2018 (62 breaches). Abnormal returns for the firms are found negative due to these privacy breaches, but AMM consistently reports more negative abnormal returns than MM for all the years. The AMM-SUR, observing event clustering, consistently reports slightly lower negative abnormal returns than the AMM for all the years. We have also calculated the average financial loss due to a privacy breach for these years and for three different models, i.e., MM, AMM, and AMM-SUR.

CVDP k-means clustering algorithm for differential privacy based on coefficient of variation

Data clustering has been applied and developed in all walks of life, which can provide convenience for enterprise service optimization. However, when the original data to be analyzed contains users’ personal privacy information, the clustering analysis process of the data holder may expose users’ privacy. Differential privacy k-means algorithm is a clustering method based on differential privacy protection technology, which can solve the privacy disclosure problem in the process of data clustering. In the differential privacy k-means algorithm, Laplacian noise controlled by privacy parameter ɛ is added to the center point of clustering to protect user sensitive information and clustering results in the original data, but the addition of noise will affect the utility of clustering. In order to balance the availability and privacy of the differential privacy k-means clustering algorithm, the research on the improvement of the algorithm pays more attention to the selection of the initial clustering center or the optimization of the outlier processing, but does not consider the different contribution degree of each dimension data to the clustering. Therefore, this paper proposes a differential privacy CVDP k-means clustering algorithm based on coefficient of variation. The CVDP scheme first eliminates outliers in the original data through data density, and then designs weighted data point similarity calculation method and initial centroid selection method using variation coefficient. Experimental results show that CVDP k-means algorithm has some improvements in availability, performance and privacy.

Privacy protection in social applications: A ciphertext policy attribute‐based encryption with keyword search

In a highly evolved big data era, intelligent data analysis can improve social operation efficiency and save resources. However, it also brings masses of conflicts, such as malicious mining and abuse of personal privacy information. This paper introduces a privacy protection scheme for social applications. In this scheme, attribute based searchable encryption is used to defend the security of confidential data and ensure the availability of data. Moreover, the access control structure of ciphertext strategy can meet the needs of data sharing in social applications. Security analysis shows that the scheme does not disclose privacy information in index ciphertext, search trapdoor, and equality test. Compared with plaintext information upload and sharing, the additional performance overhead caused by the scheme is acceptable. The scheme can be actually deployed in social applications.

Study of COVID-19 Monitoring System Based on Block Chain and Anonymity Techniques

Since the COVID-19 epidemic has been raging around the world, it has a great impact on people’s life and work; in addition to monitoring and controlling the epidemic situation, government departments are also facing the impact of economic downturn. The investigation of epidemic situation and the privacy of personal information often contradict each other. It is a headache for countries all over the world how to make both sides perfect. This study uses blockchain technology, combined with cryptography theory, proposes a set of epidemic monitoring information system, which has the function of anonymity. When hospitals or doctors release patient information, they do not need to worry about personal information leakage.

Sleep Staging Method for Imbalanced EEG Data Based on Differential Privacy Federated Learning

EEG is a personal privacy information, that is difficult to share data and not conducive to the study of sleep staging. In this paper, the Federated Learning (FL) method is introduced to solve the problem of data island in sleep staging EEG signals. To simulate the distribution of different user data, we introduce the K-means algorithm into EEG dataset partitioning and transform it into a non-independent and identically distributed unbalanced dataset. Then, based on the classic Multilayer Perceptron (MLP) network, we analyse the privacy leakage problem in FL, and Differential Privacy (DP) is used to protect the privacy of user EEG data. Next, we analyse the reason for the accuracy loss between FL and Centralized Learning (CL), and the effect of the imbalance of the data distribution of each client. We propose a self-adaptive batch method to adaptively balance the batch size of each client, and experiments show that the training results of imbalanced datasets can be improved. Finally, when the accuracy of the model does not reach the historical best value in the set epoch interval, the Mandatory Optimization Strategy (MOS) is used to further improve the accuracy and reduce the randomness of the model. The DP-FL based on the self-adaptive batch and MOS strategy is tested on our dataset and the Sleep-EDF Database Expanded dataset, which not only protects user privacy but also achieves the same accuracy as CL. The accuracy is 82.88% on our dataset and 91.81% on the subset of the Sleep-EDF Database Expanded dataset.

Wavelet-Domain Information-Hiding Technology with High-Quality Audio Signals on MEMS Sensors.

Due to the rapid development of sensor technology and the popularity of the Internet, not only has the amount of digital information transmission skyrocketed, but also its acquisition and dissemination has become easier. The study mainly investigates audio security issues with data compression for private data transmission on the Internet or MEMS (micro-electro-mechanical systems) audio sensor digital microphones. Imperceptibility, embedding capacity, and robustness are three main requirements for audio information-hiding techniques. To achieve the three main requirements, this study proposes a high-quality audio information-hiding technology in the wavelet domain. Due to the fact that wavelet domain provides a useful and robust platform for audio information hiding, this study applies multi-coefficients of discrete wavelet transform (DWT) to hide information. By considering a good, imperceptible concealment, we combine signal-to-noise ratio (SNR) with quantization embedding for these coefficients in a mathematical model. Moreover, amplitude-thresholding compression technology is combined in this model. Finally, the matrix-type Lagrange principle plays an essential role in solving the model so as to reduce the carrying capacity of network transmission while protecting personal copyright or private information. Based on the experimental results, we nearly maintained the original quality of the embedded audio by optimization of signal-to-noise ratio (SNR). Moreover, the proposed method has good robustness against common attacks.