Prevent Data Loss Research Articles

Biometric authentication utilizing fuzzy extractor with PSO based security ensuring the data security mechanism as Trio in Cloud

Cloud computing is widely preferred by various organizations due to its numerous advantages such as lower costs, better security, and faster processing times. The most critical data stored in the remote servers are protected by firewalls and encryption, at the same time, the user’s essential data may be accessed from the remote cloud server without their control. This kind of data storage facility introduces various security complications. In order to prevent these issues, we have proposed a method that uses a Biometric authentication method with PSO to provide high-level security. The method combines a pseudo-integer authentication scheme with a secret OTP that can be used to authenticate an individual. A Tri-level security mechanism model is proposed to store three types of data classification namely, No Privacy, Privacy with Trusted Provider and Privacy with Non-PNR. The proposed method provides a uniform security scheme for all the classifications using Adaptive ElGamal with Menezes-Qu-Verstone, that prevents data loss and corruption caused by insiders in the cloud. Further Seed Block is utilized to recover and extract data without loss. It does so by effectively extracting the data from the formatted disks. It is then followed with the decryption process, as if the data are requested by the user. Our framework aims to provide a secure and authenticated environment for accessing data. It features an improved authentication process and reduced complexity.

Holistic Approach in Introducing Proper Legal Framework to Regulate Data Protection and Privacy in Sri Lanka

Data protection and privacy law have never been important as they are today. Data protection and privacy ensure that data is safeguarded from unlawful access by unauthorised third parties and misappropriation of the same. A successful data protection strategy will be helpful to prevent data loss, theft, or corruption of data. It is evident that information and communication technology is developing daily and privacy issues or the threats against personal data of the persons also equally increasing. Responsibility of a government to provide effective privacy and data protection laws/policies cannot be disregarded at any point. Until very recent Sri Lanka did not have a separate legislation to deal with data protection and privacy and it was identified as a major lacuna in our law. At present, in addition to the Personal Data Protection Act No. 09 of 2022 there are several other legislations that may be applied to regulate certain aspects of data protection and privacy. In this research, researcher is aiming to assess whether existing legal framework on data protection and privacy in Sri Lanka is adequate and effective. This will be done by comparing the Sri Lankan legal framework with UK and Singapore, countries that are known as pioneers of data protection and privacy. Ultimate goal of the researcher is to contribute towards assurance of data protection and privacy right of the individuals in Sri Lanka. 
 Keywords: Data protection, Privacy, Information Communications Technology, Personal data

Fetal ultrasound image segmentation using dilated multi-scale-LinkNet

Ultrasound imaging is routinely conducted for prenatal care in many countries to determine the health of the fetus, the pregnancy's progress, as well as the baby's due date. The intrinsic property of fetal images during different stages of pregnancy creates difficulty in automatic extraction of fetal head from ultrasound image data. The proposed work develops a deep learning model called Dilated Multi-scale-LinkNet for segmenting fetal skulls automatically from two dimensional ultrasound image data. The network is modeled to work with Link-Net since it offers better interpretation in biomedicine applications. Convolutional layers with dilations are added following the encoders. The Dilated convolution is used to expand the size of an image to prevent data loss. Training and evaluating the model is done using the HC18 grand challenge dataset. It contains 2D ultrasound images at different pregnancy stages. The results of experiments performed on an ultrasound images of women in different pregnancy stages. It reveals that we achieved 94.82% Dice score, 1.9 mm ADF, 0.72 DF and 2.02 HD when segmenting the fetal skull. Employing Dilated Multi-Scale-LinkNet improves the accuracy as well as all the evaluation parameters of the segmentation compared with the existing methods.

Determination of electron trajectories within an energy analyzer using by a genetic algorithm method

Energy analyzers are designed specifically to distinguish electrons according to their energies. In these experimental devices, electron beam is focused on the entrance of the detector is vital for the prevention of experimental data loss. In this study, the optimum optical parameters when the electron beam is focused on the detector were obtained by the genetic algorithm, an innovative method. It was used to find the optical parameters of hemispherical deflector analyzers that provide the best focus on the detector. The results of this pilot study indicated that genetic algorithm gave a rapid solution when compared to the traditional methods such as Boundary Element (BEM) and Finite Difference Methods (FDM) for electron energy analyzers. Thanks to the genetic algorithm software prepared in this study, all parameters of the hemispherical deflector analyzer can be calculated simultaneously and in a short time.

Optimized Genetic Algorithm and Extended Diffie Hellman as an Effectual Approach for DOS-Attack Detection in Cloud

Cloud computing is a mode to increase competence and capabilities devoid of investing in any infrastructure. It seems that in cloud computing environment the major problem that ensure the secure communication and protect responsive data in open networks from unauthorised access. These days it seems the headlines are jam-packed with stories about security breaches to these services; that result in the leak of a large amount of private data of the users. As cloud computing can offer new computing benefits, but it faces soaring risks, specifically on the security side where DOS attacks can make cloud services unavailable. This paper aims to turn up an effective method of detecting DOS attacks with an optimized Genetic algorithm and extended version of Diffie-hellman algorithm. To prevent data loss or corruption caused by the insiders in the cloud, Optimized Genetic Algorithm (OGA) is utilized, which effectively recovers the data and retrieve it if the missed data without loss. It is then followed with the decryption process as if requested by the user. An optimized path assortment for information broadcast proves to be an effective method in the cloud computing atmosphere. The proposed framework ensures certification and paves way for secure data access in an unauthorized network, with improved performance. It successfully assure the high level of protection of the transmission and data transmitted. And concurrently reduce the communication complexities. To reduce time complexity and detect the attackers by mutual secret key that is brought on using extended version of Diffie-hellman to endorse available key generation.

A System for Trusted Recovery of Data Based on Blockchain and Coding Techniques

With the continuous development of information technology, the Internet of Things has also been widely used. At the same time, in the power Internet of Things environment, reliable data is essential for data use and accurate analysis. Data security has become a key factor in ensuring the stable operation of the power grid. However, the power Internet of Things devices is extremely vulnerable to network attacks, leading to data tampering and deletion. Resisting tampering, preventing data loss, and reliably restoring data have become difficult to ensure data security. In order to solve this problem, this paper proposes a trusted data recovery system based on blockchain and coding technology. Data nodes of the power Internet of Things encode key data and back them up to the blockchain network through a data processing server located on the edge. The data processing server performs real-time detection of the data integrity of the data nodes. When the data is tampered with or deleted, the data processing server promptly obtains the corresponding data encoding blocks from the blockchain network, decodes them, and sends them to the data node to complete the data recovery task. According to the test result, the data backup speed of this system is increased by 15.3%, and the data recovery speed is increased by 19.8% compared with the traditional scheme. It has good security and real-time performance. Meanwhile, it reduces the network and storage resource overhead in the data backup and recovery process.

Blood and Product-Chain: Blood and its Products Supply Chain Management based on Blockchain Approach

This paper provides a novel implementation of blockchain technology, and data is stored in a decentralized distributed ledger to assist information protection in blood supply chain management and prevent data loss or identity theft. The present blood supply is used exclusively from the blood of volunteers (known as donors), making blood and its derivatives one of the significant roles in treating diseases today. In particular, depending on the type of product extracted from the blood (e.g., red blood cells, white blood cells, platelets, plasma). They require different procedures and storage environments (e.g., time, temperature, humidity). However, the current blood management processes are done manually - where all medical staff does all data entry. Additionally, data about the complete blood donation process (e.g., blood donors, blood recipients, blood inventories) is held centrally and is challenging to examine accurately. Therefore, ensuring centralized data security is extremely difficult because of stealing personal information or losing data. In this study, we present the blockchain technology-based blood management process and offer Blood and Product-Chain, a decentralized distributed ledger that stores data to address these restrictions. Specifically, we target two main contributions: i) we design the Blood and Product-Chain model to manage all relevant information about blood and its products based on blockchain technology, and ii) we implement the proof-of-concept of Blood and Product-Chain by Hyperledger Fabric and evaluate this in the two scenarios (i.e., data creation and data access).

Backup & Data Recovery in Cloud Computing: A Systematic Mapping Study

Context: Digital data is being stored in large quantities in Cloud, requiring data backup and recovery services. Due to many factors such as disasters and other disruptive events, the risk of data loss is huge. Therefore, backup and data recovery are essential and effective in improvement of system availability and maintaining Business Continuity. Nevertheless, the process to achieve the goal of business uninterrupted faces many challenges regarding data security, integrity and failure prediction. Objective: This paper has the following goals: analyzing system- atically the current published research and presenting the most common factors leading to the need of Disaster Recovery and backup plan; investigating and identifying the adopted solutions and techniques to prevent data loss; and lastly, investigating the influence Data Recovery and Backup has in terms of business continuity and identifying the privacy and security issues regarding the disaster recovery process. Method: A systematic mapping study was conducted, in which 45 papers, dated from 2010 to 2020 were evaluated. Results: A set of 45 papers is selected from an initial search of 250 papers, including 10 papers from snowball sampling, following the references from some papers of interest. These results are categorized based on the relevant research questions, such as causes of disasters, data loss, business continuity, and security and privacy issues. Conclusion: An overview of the topic is presented by investigating and identifying the following features: challenges, issues, solutions, techniques, factors, and effects regarding the backup and recovery process.

Cloud security framework and key management services collectively for implementing DLP and IRM

A free and open-source framework management solution can help organizations as well as end-users. An agentless, completely controlled, centrally administered, and freely distributed source, cloud-based data leak control includes enhanced data loss protection management for both corporations and end-users. An Open-Source Framework in the cloud zone was used to construct the data loss prevention tool. This framework can be released under an Open License or a Free License, making it freely usable by end-users throughout the world. You can run it on any of these platforms: Linux, Windows, or UNIX. It relies on open database connectivity and MSSQL credentials for its database connectivity, although MySQL Server is used for database connectivity. As many as hundreds of individual files or thousands of multiple files can be accessed simultaneously by the Open User Data Loss Management Console, which can identify the case-sensitive information among them.

Towards Grading Chest X-rays of COVID-19 Patients Using a Dynamic Radial Basis Function Network Classifier

The high volume of COVID-19 Chest X-rays and less number of radiologists to interpret those is a challenge for the highly populous developing nations. Moreover, correct grading of the COVID-19 stage by interpreting the Chest X-rays manually is time-taking and could be biased. It often delays the treatment. Given the scenario, the purpose of this study is to develop a deep learning classifier for multiple classifications (e.g., mild, moderate, and severe grade of involvement) of COVID-19 Chest X-rays for faster and accurate diagnosis. To accomplish the goal, the raw images are denoised with a Gaussian filter during pre-processing followed by the Regions of Interest, and Edge Features are identified using Canny's edge detector algorithm. Standardized Edge Features become the training inputs to a Dynamic Radial Basis Function Network classifier, developed from scratch. Results show that the developed classifier is 88% precise and 86% accurate in classifying the grade of illness with a much faster processing speed. The contribution lies in the dynamic allocation of the (i) number of Input and Hidden nodes as per the shape and size of the image, (ii) Learning rate, (iii) Centroid, (iv) Spread, and (v) Weight values during squared error minimization; (vi) image size reduction (37% on average) by standardization, instead of dimensionality reduction to prevent data loss; and (vii) reducing the time complexity of the classifier by 26% on average. Such a classifier could be a reliable assistive tool to human doctors in screening and grading COVID-19 patients and in turn, would help faster management of the patients as per the stages of COVID-19.

Data Loss Prevention from a Malicious Insider

ABSTRACT This study targeted cloud service providers (CSPs) and presented a depiction of their security awareness to mitigate a malicious insider threat. CSPs must ensure the privacy of customers’ data. Cloud service customers (CSCs) place their data on the Cloud, relieving them from data security and management; however, they lose control over the data. Several organizations are concerned about the privacy of data on the cloud due to sophisticated threats like malicious insider. To reveal whether CSPs are better protecting CSCs data, a survey was conducted in New Zealand and Australia to assess the data loss prevention from a malicious insider in the Cloud. An inductive quantitative approach was used in this research, and data was collected from 79 service providers based in New Zealand and Australia. This study provides insights about insider threats risks and security recommendations are provided to CSPs’ to develop awareness and how CSPs should prevent data loss.

Remote Real-time Power Analysis and Management System

For many progressing and advancing nations, the electric energy measurement infrastructure is inadequate in terms of the lack of real-time measurement of power quality and quantity, as well as the connectivity to the central data collection station. To overcome such issues, more modern methods of sensing and distributing electrical energy must be explored and implemented. In this article, we present a LoRa-based real-time load monitoring and control system that can measure the amount (voltage, current, and power) and the quality (power factor and harmonics) of the electricity being supplied, as well as provide remote power cut and restore functionality through an IoT SCADA server. The proposed system included a microcontroller-based Data Acquisition (DAQ) system, a LoRa radio-based Data Communication Network (DCN), a Raspberry Pi-based Online Gateway, and an online SCADA server to gather and present data. The system is powered by the main power line, which is also being measured, and a battery backup system to prevent data loss in the event of a power outage or fault. Through Proteus and MATLAB simulations, the system was evaluated before being implemented on the hardware, and the results of both are described. The system was energy efficient, owing to the usage of the LoRa wireless technology, and was accurate in analyzing the power consumption characteristics of loads in real-time, tested in comparison to standard laboratory equipment.

Optimizing the Roadside Unit Deployment Mechanism in VANET with Efficient Protocol to Prevent Data Loss

The vehicular ad hoc network, combined with the distribution of roadside units (RSUs), play a significant role in reducing accidents and traffic jams by sending warning and emergency messages to drivers in real-time. However, communication is not always available owing to the lack of RSUs, reliability of data access and control, to avoid such problems we have proposed a new strategy for the deployment of least possible number of RSUs by developing a support protocol in case of loss of communication that relies on smartphones. The simulation results show that the proposed distribution method clearly reduces the number of RSUs required (compared to existing methods), and contributes towards increased coverage, reduced costs, and less missing data.

A Review of the Literature Organized Into a New Database: RHeference.

Hundreds of articles containing heterogeneous data describe D variants or add to the knowledge of known alleles. Data can be difficult to find despite existing online blood group resources and genetic and literature databases. We have developed a modern, elaborate database for D variants, thanks to an extensive literature search with meticulous curation of 387 peer-reviewed articles and 80 abstracts from major conferences and other sources. RHeference contains entries for 710 RHD alleles, 11 RHCE alleles, 30 phenotype descriptions (preventing data loss from historical sources), 35 partly characterized alleles, 3 haplotypes, and 16 miscellaneous entries. The entries include molecular, phenotypic, serological, alloimmunization, haplotype, geographical, and other data, detailed for each source. The main characteristics are summarized for each entry. The sources for all information are included and easily accessible through doi and PMID links. Overall, the database contains more than 10,000 individual pieces of data. We have set up the database architecture based on our previous expertise on database setup and biocuration for other topics, using modern technologies such as the Django framework, BioPython, Bootstrap, and Jquery. This architecture allows an easy access to data and enables simple and complex queries: combining multiple mutations, keywords, or any of the characteristics included in the database. RHeference provides a complement to existing resources and will continue to grow as our knowledge expands and new articles are published. The database url is http://www.rheference.org/.

Mobile application design of smart water supply chain based on IoT: A case study in Indonesia

Some areas in Indonesia often lack clean water even occur drought if the dry season strikes. However, several other regions of Indonesia have pure water abundance despite the dry season. Based on this problem, the authors aimed to design a mobile application of a smart water supply chain based on the Internet of Things (IoT) for admin and users. The app was Control Your Water (C_Water) application. The app developed aimed not only to detect water shortage quickly but also to accommodate online water purchasing. By installing sensors in the water reservoirs, the admin could find out the supply of clean water in the tanks. Information had obtained from the sensor will go to Microcontroller, then the Microcontroller will send it to the database via Wifi. The mobile application displayed information based on data in the database. The authors proposed a data backup process to prevent data loss. The authors designed a water reservoir consisting of positions of ultrasonic sensors and a division of water level. The result of this research finds that this design helps the government (admin) to overcome the problem of real water shortages that occur in Indonesia.

Relevance of Zero Trust Network Architecture amidts and it’s rapid adoption amidts Work From Home enforced by COVID-19

As organizations evaluate different methods to improve data security, they have to shift to find the best alternative means to secure the servers. Zero Trust Network Architecture (ZTNA) has become the focus of many institutions to prevent data loss, especially from the employees working remotely due to the spread of Covid-19, requiring people to keep social distance minimize risks of an attack. Zero trust employs a multifactor authentication process that requires all users to verify their identity or the device they are using before using a network. This security procedure has been useful in eliminating the castle-and-moat concept's weaknesses that were initially used and could allow an individual to navigate freely through a network once they penetrated the firewall. The Zero trust does not allow the penetration and further segment of different materials; therefore, an individual cannot access all materials once in a network. This cybersecurity method will be advantageous post COVID-19 to deliver a simplified user experience enabling them to manage and find the contents with ease. For the customers, the architecture creates a uniform platform that can be used to amplify the security. The technology is a must for cost-benefit analysis for every organization considering its potential to help avoid potential security and financial losses

A Beginner's Guide to Conducting Reproducible Research

A Beginner's Guide to Conducting Reproducible Research

Data Pseudonymization in a Range That Does Not Affect Data Quality: Correlation with the Degree of Participation of Clinicians.

Personal medical information is an essential resource for research; however, there are laws that regulate its use, and it typically has to be pseudonymized or anonymized. When data are anonymized, the quantity and quality of extractable information decrease significantly. From the perspective of a clinical researcher, a method of achieving pseudonymized data without degrading data quality while also preventing data loss is proposed herein. As the level of pseudonymization varies according to the research purpose, the pseudonymization method applied should be carefully chosen. Therefore, the active participation of clinicians is crucial to transform the data according to the research purpose. This can contribute to data security by simply transforming the data through secondary data processing. Case studies demonstrated that, compared with the initial baseline data, there was a clinically significant difference in the number of datapoints added with the participation of a clinician (from 267,979 to 280,127 points, P < 0.001). Thus, depending on the degree of clinician participation, data anonymization may not affect data quality and quantity, and proper data quality management along with data security are emphasized. Although the pseudonymization level and clinical use of data have a trade-off relationship, it is possible to create pseudonymized data while maintaining the data quality required for a given research purpose. Therefore, rather than relying solely on security guidelines, the active participation of clinicians is important.

TECHNOLOGIES OF USER ACTIVITIES MONITORING AND ANALYSIS IN PREVENTING INSIDER THREATS OF INFORMATION SECURITY OF AN ORGANIZATION

The increase in the number of information security incidents related to personnel activities, the frequency of which has almost doubled in the last two years, has led organizations to use effective technologies that prevent and counteract internal threats to information security. An important role in this context belongs to the tools of monitoring and analysis of user activity. According to experts, in the coming years, such technologies will be implemented in 80% of solutions to identify threats and prioritize information security incidents. The article reveals the essence and analyzes the functionality of several systems that monitor and analyze employee behavior, including Data Loss Prevention (DLP), Access Control, Analysis of User Behavior and IT objects (UBA / UEBA). The authors establish that the DLP system monitors and reports on user attempts to transmit confidential information by monitoring mail and web traffic, wireless access, external storage, input/output devices, user workstation software, audio and video surveillance of its activities, etc. Access control tools perform, in particular, the functions of monitoring access and movement of a person in protected areas of the object, collecting information from surveillance cameras, keeping records of working time. In the context of a pandemic, solutions have been developed that allow identifying a person in a mask on the face, to perform the functions of monitoring health. Analysis of the functional characteristics of UBA / UEBA behavioral analytics systems showed that they not only solve the problem of collecting data from all possible available sources (software and hardware, logs, user correspondence, etc.), but also analyze the collected data and report atypical user behavior in case of its detection. The article notes that behavioral analytics is used in a number of security technologies, such as Security Information and Event Management system, Intrusion Detection and Prevention System, and others, complementing and expanding their capabilities, helping to create comprehensive information security solutions. The authors recommend organizations to use tools for monitoring and analyzing the user activities in different combinations or as part of integrated Information Security Management solutions to achieve the appropriate information security level in the face of growing threats from personnel.

Increasing Security of Database During Car Monitoring

Nowadays, a vast data flow causes quite a problem in several sectors. One of the issues related to information growth and data volume is their appropriate storage and the securing of the data processing related security and its protection. In this era, where the data are a very sensitive commodity, an attack or data loss can mean losing user trust. Various researchers have dealt precisely with these ideas. They figured out several effective methods of preventing data loss and how to reduce attack amount either on a database or on a web site. Big data, which currently has a significant role in many statistics, are susceptible to various data losses, steal, or deletion. Possible data deletion can lead to a wrong decision about a transport situation prediction task, inaccurate result in decision support, or even to lousy diagnosis prediction in medicine. This vast amount of the problems led to the significant data amount and their security problem solution. Our article relates to data security in situations with substantial real data amount in time, which are entering the system. We suggest effective data distribution methods and a mechanism for data transfer from a database to data storage according to individual records’ time parameters.