Search

Graph databases have received increased interests as many applications are handled as graph problems. Shortest distance queries are one of the fundamental operations and have been studied for recent years. To ensure the data and query privacy, researchers have introduced some secure graph encryption schemes which support the shortest distance queries on a large-scale graph database. Unfortunately, most of them only provide an approximate result by pre-computing and storing a distance oracle. To provide the exact shortest path, our solution employs a distributed two-trapdoor public-key crypto-system to perform addition and comparison operations over ciphertexts. The detailed security analysis indicates that our scheme achieves semantically secure under DDH assumption and the experiments are performed on various real-world database and random database. The experimental result shows the feasibility of our scheme. [ABSTRACT FROM AUTHOR]

Objective: Develop and investigate the potential of a remote, computer-mediated and synchronous text-based triage, which we refer to as InSort, for quickly highlighting persons of interest after an insider attack. Background: Insiders maliciously exploit legitimate access to impair the confidentiality and integrity of organizations. The globalisation of organisations and advancement of information technology means employees are often dispersed across national and international sites, working around the clock, often remotely. Hence, investigating insider attacks is challenging. However, the cognitive demands associated with masking insider activity offer opportunities. Drawing on cognitive approaches to deception and understanding of deception-conveying features in textual responses, we developed InSort, a remote computer-mediated triage. Method: During a 6-hour immersive simulation, participants worked in teams, examining password protected, security sensitive databases and exchanging information during an organized crime investigation. Twenty-five percent were covertly incentivized to act as an 'insider' by providing information to a provocateur. Results: Responses to InSort questioning revealed insiders took longer to answer investigation relevant questions, provided impoverished responses, and their answers were less consistent with known evidence about their behaviours than co-workers. Conclusion: Findings demonstrate InSort has potential to expedite information gathering and investigative processes following an insider attack. Application: InSort is appropriate for application by non-specialist investigators and can be quickly altered as a function of both environment and event. InSort offers a clearly defined, well specified, approach for use across insider incidents, and highlights the potential of technology for supporting complex time critical investigations. [ABSTRACT FROM AUTHOR]

This study analyzes whether femicide in Mexico has increased more severely than other life and bodily integrity crimes (e.g., homicide, culpable homicide, injuries, malicious injuries, abortion, and other crimes that threaten life). To achieve this, the Executive Secretariat of the National Public Security System database was cleaned and the number of femicides per 100,000 inhabitants was calculated, for the period from January 2016 to March 2022 in all states of Mexico. Through descriptive statistics, non-parametric analysis of means, and hypothesis tests, we demonstrate that the states with the highest number of femicides are the Estado de Mexico (State of Mexico), Ciudad de Mexico (Mexico City), and Veracruz; moreover, the number of femicides exhibits a growing trend while the total number of life and bodily integrity crimes does not. Finally, we forecast the number of femicides for the next five months. To our knowledge, there is no other article that analyzes the growth trend of femicide compared to other crimes. Visualizing and understanding that femicide is on the rise compared with other types of crimes can help the government and legislators generate policies that are consistent with the magnitude of the problem. [ABSTRACT FROM AUTHOR]

The pursuit of environmental sustainability, energy conservation, and emissions reduction has become a global focal point. Electricity is the primary source of energy in our daily lives. Through the analysis of smart power systems, we can efficiently and sustainably harness electrical energy. However, electric power system data inherently contain a wealth of sensitive user information. Therefore, our primary concern is protecting these sensitive user data while performing precise and effective analysis. To address this issue, we have innovatively proposed three granular information models based on differential privacy. In consideration of the characteristics of enterprise electricity consumption data and the imperative need for privacy protection, we implement a reasonable modeling process through data processing, information granulation expression, and the optimization analysis of information granularity. Our datasets encompass enterprise electricity consumption data and related attributes from Hitachi Building Technology (Guangzhou) Co., Ltd's cloud computing center. Simultaneously, we have conducted experiments using publicly available datasets to underscore the model's versatility. Our experimental results affirm that granular computation can improve the utility of differential privacy in safeguarding data privacy. [ABSTRACT FROM AUTHOR]

Due to the COVID-19 virus, many educational institutions now encourage online learning. The National Program of Technology Enabled Learning (NPTEL) is a web portal that is used for e-learning applications. With this online course, students can access the lectures of all the respected experts from the best universities at any time and from any location. Due to privacy and security issues, many educational systems are hesitant to adopt the cloud. To avoid security issues, in this paper, a trust-based access control data hybrid cryptography model is proposed. The proposed system mainly focused on data confidentiality and the authentication process. For data security, the hybrid Attribute-Based Encryption and Elliptical Curve Cryptography (ABE-ECC) algorithm is presented. Besides, for authentication, trust-based access control is introduced. The trust of the user is calculated using three parameters: the number of successful/failed interactions, the service satisfaction index, and the level of dishonesty. The performance of the proposed method is analyzed based on different metrics, namely throughput, latency, successful rate, service utilization, encryption time, decryption time, and retrieval time. [ABSTRACT FROM AUTHOR]

Multi-user computer environments pose potential threats to users data in organisations, in that unauthorised subsequent users who log on to the same computer could leak, alter or delete data belonging to users who previously logged in to the same computer. Such a threat is inspired by Locard's exchange principle, which states (in its digital form) that every interaction with a system must ultimately leave some trace, and as a result, such trace could carry with it sensitive information that subsequent interactions may obtain without authorisation. Therefore, we attempt in this paper to define a subsequent users analysis that calculates this potential loss in data security based on data visibility and sensitivity values. We outline how such analysis can be used in the real world to enhance decision making process when logging in to a shared computer. We adopt a data-driven approach in defining our analysis and we demonstrate the validity of the analysis over a large open Cybersecurity dataset, which associates users with computers. [ABSTRACT FROM AUTHOR]

This research suggests a unique ECG security solution based on the data pattern-based encryption paradigm to address security issues in ECG signal transmission or storage environments. According to this concept, the encryption system controls the data security process by extracting key signatures from the ECG signal peaks to identify the characteristics or attributes of the data and encrypt it. To estimate the hashing qualities, the approach is to build a random key pattern based on the signal peaks and extract the signature. The signal was then encrypted and sent to a doctor or signal analyzer. Bilateral random hashing (BRH) is used to achieve this. This updates the hashing generation model by analyzing the parameters using probabilistic distributional characteristics. This crucial motif was applied. This key pattern was utilized to encrypt the data samples using the downhill peak follow (DPF) encryption technique, which follows the strength of the peak value in the ECG waveform. This suggested project is carried out using MATLAB scripting. [ABSTRACT FROM AUTHOR]

The new and integrated technologies have changed the traditional systems and services with new and smart automation systems. Smart healthcare system is one of the example of patients monitoring systems with more smart and cost effective services to protect the people who suffered from physical or psychological diseases, illnesses, and health concerns. wireless body area network contains smart sensor nodes planted inside or outside the patient body by using sensing and monitoring capabilities. With various benefits, these networks have some serious security challenges where the patient data is compromised due to different types of security attacks. Biomedical devices and sensor nodes are communicating internally or externally and always the open and easy target for attackers. There are various types of routing protocols have been designed for security but due to their complex and heavy processes the sensor nodes energy depleted, delay and overhead occurred. Due to limited resources of sensor nodes like battery, processing, memory, and storage, these networks need special lightweight security solutions. In this context, this paper proposes an interoperability and security for data authentication scheme for body area network protocol by using different leftover features like forwarding data security and address the synchronization and key control issues. The proposed protocol provides security for both internal and external devices and protect the data from any kind of unauthorized data breach. Experiments results indicated the better performance of the proposed protocol as compared with the state-of-the-art schemes. The proposed protocol is one of the effective security addition for secure medical healthcare data transmission. [ABSTRACT FROM AUTHOR]

This study evaluates mechanisms to secure relational databases against security vulnerabilities and utilized PostgreSQL and Microsoft SQL Server due to data breach incidences reported across the world. Emulation experiments with documentary review were employed to collect necessary study data. Identification of security features and vulnerabilities that are found to affect the data tier of the web applications were examined. The findings from the study have shown that Microsoft SQL Server is more security feature-rich in terms of Confidentiality, Integrity, and Availability compared to PostgreSQL and Microsoft SQL Server is more resilient to security attacks in its default behavior compared to PostgreSQL. [ABSTRACT FROM AUTHOR]

Organizations today are employing databases on a large scale to store data essential for their functioning. Malicious access and modifications of the databases may lead to adverse financial and legal implications. In recent years, security researchers have focused on detecting abuse of access privileges by employees of an organization. Identifying threats from insiders is hard because they are aware of the organization of the database in addition to having authorised access privileges. To detect insider attacks effectively and efficiently, we present a novel approach to dynamically determine the malicious transactions using historical data. We propose Trust factor-based user behavior analysis using sequential pattern mining for database intrusion detection systems (TFUBID). Since, groups of users access the organizational database for similar purposes, we cluster user behavior vectors using fuzzy clustering and define a class of Integral Data Attributes using sequential pattern mining to model trust factor-based behavioral patterns of employees accessing the database assigning higher weight to critical elements and Directly Correlated Attributes. A comprehensive experimental evaluation on our synthetic dataset adhering to TPC-C standard benchmark revealed that TFUBID achieved an accuracy of 94% for detecting malicious transactions and outperforms competing state-of-the-art techniques on several performance measures. [ABSTRACT FROM AUTHOR]

The demand for a secured web storage system is increasing daily for its reliability which ensures data privacy and confidentiality. The proposed paper aims to find the most secure ways to maintain integrity and protect privacy and security in healthcare management systems. The Advanced Encryption Standard (AES) algorithm is used to encrypt data transferred by providing a means to check the integrity of information transmitted and make it more immune to cyberattack techniques, this was implemented by using Keyed-Hash Message Authentication Code (HMAC) and Secured Hash Algorithm-256 (SHA-256). The risk of exposure to attackers can be avoided by using honeypot systems combined with Intrusion detection systems (IDSs) as a firewall system is not effective against such attacks alone. The experimental results evaluate the proposed security health information management system by comparing the performance of the encryption algorithm based on encryption time, memory and CPU usage, and entropy for different plaintext lengths. In addition, it can be seen that when changing the AES key size, more memory and time are required the longer the key size is used. The 128 bits AES key is therefore advised if the system must operate in hard real-time. [ABSTRACT FROM AUTHOR]

Energy security is a global and strategic national economic and social development issue. It is one of the most urgent challenges in global development. This study takes China as an example to identify the influencing factors of energy security comprehensively. Using natural language processing, social network analysis, and machine learning methods to mine the authoritative media network news text to identify the influencing factors of energy security and their relationships more objectively, comprehensively, and accurately. Seven major energy security themes are proposed. This study provided a reference for risk management and establishing an energy security prediction and early warning mechanism. [ABSTRACT FROM AUTHOR]

Blood donation is required for saving human lives. Blood donor needs to check blood bank or donation camp for giving blood donation or blood booking. Blood donor may have doubt whether their personal data and medical histories are safely deposited and protected. For this purpose, proposed work uses cloud techniques to develop an online blood bank system and applying Advanced Encryption Standard (AES) algorithm for database security and encryption. Proposed system maintains central repository holding various blood deposits accessible along with related particulars accommodated on a cloud server. These particulars contain type of blood group, area of storage, date and location etc., This information support in maintaining and monitoring the deposits of blood. Proposed AES based blood bank system using cloud techniques is an online system that consents handlers to check whether the essential blood deposits of a particular group are available in the blood bank capably using a cloud server. Additionally, the system also has extra features such as patient details, blood booking and even the need for a certain blood group is posted on the website to find available donors for a blood emergency. Apart from this, system includes the concepts of database encryption using AES Algorithm to make sure that the security and confidentiality of users' information. This will help us keep their donation records protected from any threats from individuals with potentially malicious intentions, or any unforeseen hazards to the security of the data. Main aim of the proposed work is to reduce the time required to deliver obligatory blood to the needy in cases of emergency. AES Algorithm will help us keep their donation records protected from any threats from individuals with potentially malicious intentions, or any unforeseen hazards to the security of the data. This AES Based blood bank system using Cloud techniques is an online system that helps in handling several blood bank procedures successfully. [ABSTRACT FROM AUTHOR]

Cloud computing refers to a set of hardware and software that are connected to provide various computing services. Cloud consists of services to deliver software, infrastructure and platform over the internet depending on user demand. However, various categories of vulnerabilities and threats are increased, due to the improved demand and development of cloud computing. In cloud computing, data integrity and security are the main issues, which is reduced the system performance. Therefore, an Enhanced Elman Spike Neural Network (EESNN) based Fractional Order Discrete Tchebyshev moments (FrDTMs) encryption fostered big data analytical method is proposed in this manuscript for enhancing cloud data security. Initially, the input data is pre-processed using a Z-score normalization process. Also, the optimal features are implemented by Uni-variate ensemble feature selection technique, which can improve the quality and reliability of data. The proposed EESNN model is used to classify the attack types in cloud databases and the security is improved by FrDTMs model. The proposed EESNN-FrDTMs approach has achieved high data security and performance. The simulation of this model is done using JAVA. From the simulation, the proposed model attains 11.58%, 26.6%, 25.5%, 45.75%, 36.88% high accuracy, 7.37%, 15.43%, 8.68%, 11.42%, 16.88% lower information loss than the existing approaches, like PSO-PNN, eHIPF, GSNN-FEO, and BISNN-BO respectively. [ABSTRACT FROM AUTHOR]

Background: Attacks on health care represent an area of growing international concern. Publicly available data are important in documenting attacks, and are often the only easily accessible data source. Data collection processes about attacks on health and their implications have received little attention, despite the fact that datasets and their collection processes may result in differing numbers. Comparing two separate datasets compiled using publicly-available data revealed minimal overlap. This article aims to explain the reasons for the lack of overlap, to better understand the gaps and their implications. Methods: We compared the data collection processes for datasets comprised of publicly-reported attacks on health care from the World Health Organization (WHO) and Insecurity Insight's Security in Numbers Database (SiND). We compared each individual event to compile a comparable dataset and identify unique and matched events in order to determine the overlap between them. We report descriptive statistics for this comparison. Results: We identified a common dataset of 287 events from 2017, of which only 33 appeared in both datasets, resulting in a mere 12.9% (n = 254) overlap. Events affecting personnel and facilities appeared most often in both, and 22 of 31 countries lacked any overlap between datasets. Conclusions: We conclude that the minimal overlap suggests significant underreporting of attacks on health care, and furthermore, that dataset definitions and parameters affect data collection. Source variation appears to best explain the discrepancies and closer comparison of the collection processes reveal weaknesses of both automated and manual data collection that rely on hidden curation processes. To generate more accurate datasets compiled from public sources requires systematic work to translate definitions into effective online search mechanisms to better capture the full range of events, and to increase the diversity of languages and local sources to better capture events across geographies. [ABSTRACT FROM AUTHOR]

Quantum Private Information Retrieval (QPIR) allows a user (Alice) to retrieve a database item from the database owned by the database holder (Bob) in such a way that Alice can query only the database item she wants, but cannot get other items, and Bob does not know which item Alice queries. However, the real quantum channel between Alice and Bob is noisy, and the noise may result in not only the chance that Alice obtains a false item, but also that both parties may cheat by camouflaging themselves with noise. In this paper, we use the decoherence-free subspace (DFS) for QPIR, which we call DFS-QPIR. The DFS-QPIR protocol removes the effect of the channel noise on the errors in the retrieved item so that two parties cannot cheat by replacing the noisy channel with a noiseless one. It can work over a collective noisy channel while retaining high reliability, database security and user privacy simultaneously. While only the collective unitary noise is taken into account, the proposed DFS-QPIR protocol can be straightforwardly extended to more general collective noise channel. [ABSTRACT FROM AUTHOR]

The vulnerability patch R&D has become an important part of information security governance. An effective collaboration with software vendors in patch R&D is of great significance to reduce the existence time of information security risks. This works aims to explore the relationship between vulnerability information disclosure and patch R&D of software vendors. The data regarding the vulnerability and software vendors is gathered from third-party vulnerability sharing platforms, including (China's national information security vulnerability database, CNNVD) and Tianyacha.com. Based on the theory of organizational information processing, linear regression model and Cox proportional risk regression model are built for appropriately addressing the research questions. The results show that the vulnerability disclosure of the third-party sharing platform can improve the patch R&D probability of software vendors. The information processing requirements, such as vulnerability information attention, vulnerability score and whether vulnerabilities are disclosed in advance accelerate the vulnerability patch R&D. The enterprise information processing capability indicators, including the industry dependence of software product customers and the staff size of software vendors accelerate the patch R&D. The number of products affected by the vulnerabilities and the number of software copyrights of software vendors have no significant impact on patch R&D. [ABSTRACT FROM AUTHOR]

Databases are perhaps the most attacked and vulnerable elements in the online environment as far as organizations are concerned. The databases contain the most important information of an organization and its confidential data, along with those of its customers. However, little of the effort dedicated to security is intended to protect data. The present paper draws attention to the need to implement a simple and then complex security in a database using RDF (Resource Description Framework) based descriptions. Our example is one implemented for Microsoft Access and covers different security level descriptions considering attack types. We choose to represent the basic concepts and abstract syntax using the RDF data modeling vocabulary and we're using as a starting point a legacy relational database where the company already catalogued attacks. [ABSTRACT FROM AUTHOR]

Navigation safety issues are of common interest to all maritime countries due to their actions in a complex and risky environment. The world's most serious ship accidents can cause major economic losses, loss of life, and severe environmental pollution in the sea. The data included in trust and security databases are usually collected from different sources. The accuracy of the collected data may vary from source to source due to a greater number of external and internal factors such as different manufacturers, and working and environmental conditions. The quality of the data may vary due to the incompleteness of the details, subjectivity, and experience of the data collector. Databases on the frequency of failures, the estimation of which can be based on recorded failure events, are difficult to access. Therefore, the aim of the research, based on the available statistical indicators of maritime risks and safety, is to indicate the application of mathematical statistics, analysis and assessment of unwanted events. The statistical methods proposed in the paper aim to point out the importance of prediction and the possibility of improving safety. In this sense, the polynomial function was applied. To visualize the results obtained during the research, the Excel program is used. [ABSTRACT FROM AUTHOR]

Database systems have become imperative for organisations around the world to store and analyse information. However, as one of the ramifications of a massive surge in cloud-based activities and interactions brought forth by the advent of the internet era, the data is exposed to an audience broader than ever and there are a variety of new challenges putting database security in jeopardy. To be able to address the problem of data security, we propose a unique method for Database Intrusion Detection System build on frequent sequential pattern mining and a modified metaheuristic hybrid clustering of Grey Wolf and Whale optimization algorithm to determine malicious transactions in Role Based Access Control and non-RBAC supervised databases. Our proposed approach extracts data dependency rules from the database logs using CM-SPADE mining algorithm to detect outsider threats. It then assigns role profiles to the users based on the previous user activities using the modified metaheuristic clustering to detect insider threats. Thereby, identifying incoming transactions as malicious by matching the role profile of the user and comparing the adherence of the transaction pattern to the generated dependency rules. To evaluate the efficiency of the model we generated a synthetic dataset including malicious and non-malicious transactions adhering to the TPC-C benchmark, and the findings were encouraging, with levels of accuracy of around 97.8 percent. [ABSTRACT FROM AUTHOR]

This study sought to design a framework to enhance database security in state-owned universities. Database security has been a cause for concern for state-owned universities following many reported cases of state-owned universities that were repeatedly hacked locally and the fact that internationally and there seems to be no strategy in place to guide database security mechanisms in state-owned universities. The study followed a positivist research philosophy using a hypothetical model to test various hypotheses. Through the lens of activity, Social Technology System and deterrence theories, using a survey method to gather the information, the hypotheses were tested and analyzed to further understand security vulnerabilities, security controls and ideal architectural requirements of database security. Quantitative data were collected from 104 respondents using questionnaires. A deductive approach was employed to ensure that the proposed strategy was designed after collecting the necessary data. The quantitative data were analyzed using SPSS version 22. The research findings highlighted the following issues that affect database security: human factor, work environment and technology in use. These issues lead to data exposure, data theft, data corruption, loss of revenue and loss of goodwill. Finally, a framework was proposed that would enhance database security in state-owned universities. [ABSTRACT FROM AUTHOR]

The industrial internet of things (IIoT), a leading technology to digitize industrial sectors and applications, requires the integration of edge and cloud computing, cyber security, and artificial intelligence to enhance its efficiency, reliability, and sustainability. However, the collection of heterogeneous data from individual sensors as well as monitoring and managing large databases with sufficient security has become a concerning issue for the IIoT framework. The development of a smart and integrated IIoT infrastructure can be a possible solution that can efficiently handle the aforementioned issues. This paper proposes an AI-integrated, secured IIoT infrastructure incorporating heterogeneous data collection and storing capability, global inter-communication, and a real-time anomaly detection model. To this end, smart data acquisition devices are designed and developed through which energy data are transferred to the edge IIoT servers. Hash encoding credentials and transport layer security protocol are applied to the servers. Furthermore, these servers can exchange data through a secured message queuing telemetry transport protocol. Edge and cloud databases are exploited to handle big data. For detecting the anomalies of individual electrical appliances in real-time, an algorithm based on a group of isolation forest models is developed and implemented on edge and cloud servers as well. In addition, remote-accessible online dashboards are implemented, enabling users to monitor the system. Overall, this study covers hardware design; the development of open-source IIoT servers and databases; the implementation of an interconnected global networking system; the deployment of edge and cloud artificial intelligence; and the development of real-time monitoring dashboards. Necessary performance results are measured, and they demonstrate elaborately investigating the feasibility of the proposed IIoT framework at the end. [ABSTRACT FROM AUTHOR]

A web application is a dynamic, intricate, and interactive program that provides end-users with information and services such as utility payments, online communication, e-learning, socializing, shopping, online banking, and income tax filing etc. Web applications have become a major target for attackers due to their accessibility, availability, and ubiquity. Web application vulnerabilities are hazardous for some reasons. Attackers can harm an organizations image and status. The implementation flaws in web application allow the invader to infuse user-input that violates the syntax-based assembly of the query or infuse malicious code etc. Among various types of injection flaws, SQL injection (SQLI) is more prominent than (XML) both are considered as common application-layer web attack, which allows the attacker to bypass the security mechanisms therefore; these two are ranked as the most common vulnerabilities. Hence, a methodology for detecting evaluating both SQLI & XML vulnerabilities in web applications are considered for research. This research work addresses the above mentioned flaws and proposed an Ensemble Method to classify the Structure Query Language injection vulnerabilities, we selected a benchmark dataset with 33,758 rows containing; various types of SQL and XML injection attacks. Raw data is preprocessed to remove artifacts, and then feature engineering is performed using Natural Language Processing techniques to clean the data and extract 6 types of features such as TF-IDF, Word-to-Vector, SkipGram, Count Vectorizer, Glove and Continuous Bag of words. Imbalance data is handled using sampling techniques, best features are selected using 4 types of validation techniques Significant Test, PCA, Variance Threshold and Sbest. Prepared data is provided to Ensemble Model having two stages; Stage-2 accepts URL from the user and detects presence of susceptibility in the sub domains and domains. Stage-1 having 9 different types of machine learning models Multinomial, Gaussian, Bernoulli Naive Bayes, Logistic Regression, Decision Tree, Random Forest, AdaBoost, SVC with, poly, rbf and linear kernel, these models are trained on additional vectors such as google news and glove to detect the new query either SQL or XML for presences or absence of vulnerability, using this proposed ensemble approach obtained the accuracy of 99%. [ABSTRACT FROM AUTHOR]

Objectives: Security of vector spatial data is of importance in the community of geographic information sciences. It is necessary to encrypt the vector spatial data based on the consideration of data security. The existing method is to encrypt the entire data file, which affects the view of attribute data and destroys vector spatial data structure. Methods: A coordinate encryption method which does not change the structure of vector spatial data file is proposed, which not only protects the security of data, but also has no impact on the normal use of data. Firstly, vector spatial data are encrypted in Haar transform domain by the secret key. The secret key used to encrypt the coordinate data is generated combined Hash key with Gaussian random number. The average coefficient and the differential coefficient are obtained from Haar transformation of the vertex sequence of vector spatial data and are encrypted by the secret key. After that, the encrypted coordinates are obtained by Haar inverse transformation of data. Finally, the Gaussian random number is utilized to scramble the vertex sequence in order to get the encrypted disordered vector spatial data. Results: The experimental results show that: (1)The structure and attribute data of the vector spatial data file are completely unchanged after encryption, only the coordinates are encrypted, and efficiency is improved. (2) The algorithm can effectively decrypt and restore the original vector spatial data, with high security. Conclusions: The proposed method can not change the structure of vector spatial data, and the efficiency of encryption is improved. [ABSTRACT FROM AUTHOR]

In this article, we explore the security politics of EU database interoperability, inquiring how knowledge infrastructures underpin European security integration. Sitting at the intersection of Science and Technology Studies (STS) and critical approaches to European security, we unpack the co-constitutive relation between database anxieties and interoperability mechanisms. By database anxieties, we refer to what European institutions identify as the main epistemic and operational concerns that emerge from the current use of databases by security authorities across Europe. These anxieties are expected to be resolved by mechanisms that foster interoperability. We argue that the relation between database anxieties and interoperability mechanisms shapes the novel conditions of possibility for European security integration in a datafied world. While far-reaching in technological terms, interoperability is not about introducing a new overarching system, but about the management, re-organisation and re-purposing of datasets. Such formatting matters politically because it eventually informs sovereign acts of policing and mobility control. [ABSTRACT FROM AUTHOR]

In this manuscript proposes an efficient big data security analysis on HDFS based on the combination of Improved Deep Fuzzy K-means Clustering (IDFKM) Algorithm and Modified 3D rotation data perturbation algorithm using health care database. To compile a similar group of data, an Improved Deep Fuzzy K-means Clustering (IDFKM) Algorithm is used as partitioning the medical data. After clustering, Modified 3D rotation data perturbation technique is used to satisfy the privacy requirement of the client. Modified 3D rotation Data Perturbation technique perturbs each and every sensitive data of the cluster and all the key parameters values used for clustering have warehoused in the database file sector. The proposed approach is executed by Java program, its efficiency is assessed by Health care database. The metrics under the study of memory usage attains higher accuracy 34.765%, 23.44%, 52.74%, 18.74%, lower execution time 35.23%, 23.76%, 27.86%, 27.76%, higher Efficiency 26.85%, 38.97%, 28.97%, 35.65%. then the proposed method is compared with the existing methods such asSecurity Analysis of SDN Applications for Big Data with spoofing identity, Tampering with data, Repudiation threats, Information disclosure, Denial of service and Elevation of privileges (STRIDE), Big Data Analysis-based Secure Cluster Management for using Ant Colony Optimization (ACA) Optimized Control Plane in Software-Defined Networks, System Architecture for Secure Authentication and Data Sharing in Cloud Enabled Big Data Environment using LemperlZivMarkow Algorithm (LZMA) and Density-based Clustering of Applications with Noise (DBSCAN), Big Data Based Security Analytics using data based security analytics (BDSA) approach for Protecting Virtualized Infrastructures in Cloud Computing respectively. [ABSTRACT FROM AUTHOR]

This study provides an in-depth analysis and research on multimedia fusion privacy protection algorithms based on IoT data security in a network regulation environment. Aiming at the problem of collusion and conspiracy to deceive users in the process of outsourced computing and outsourced verification, a safe, reliable, and collusion-resistant scheme based on blockchain is studied for IoT outsourced data computing and public verification, with the help of distributed storage methods, where smart devices encrypt the collected data and upload them to the DHT for storage along with the results of this data given by the cloud server. After testing, the constructed model has a privacy-preserving budget value of 0.6 and the smallest information leakage ratio of multimedia fusion data based on IoT data security when the decision tree depth is 6. After using this model under this condition, the maximum value of the information leakage ratio of multimedia fusion data based on IoT data security is reduced from 0.0865 to 0.003, and the data security is significantly improved. In the consensus verification process, to reduce the consensus time and ensure the operating efficiency of the system, a consensus node selection algorithm is proposed, thereby reducing the time complexity of the consensus. Based on the smart grid application scenario, the security and performance of the proposed model are analyzed. This study proves the correctness of this scheme by using BAN logic and proves the security of this scheme under the stochastic prediction machine model. Finally, this study compares the security aspects and performance aspects of the scheme with some existing similar schemes and shows that the scheme is feasible under IoT. [ABSTRACT FROM AUTHOR]

Data security and protection is one of the most common technologies used in the field of computer science. This is because of its great importance in all fields related to daily life, whether political, economic, and other aspects change. In this research paper, a new intelligent system that concentrates on image and text encryption and decryption is proposed. Furthermore, the image recognition rate is increased. In this paper, the Rivest Shamir Adleman 3 key (RSA3k) algorithm is used but in a new technique to encrypt and decrypt text. The proposed technique relies on using square root of public key as private key. XOR operation for colored images encryption and decryption is performed. By calculating signal to noise ratio (PSNR), mean square error (MSE), and bit error rate (BER), the most important things that we address how to provide data security for large data based on encryption techniques and increase the security of multimedia data and texture date and address the problem of time and accuracy in image matching. the results show that the quality of images after decryption is good. [ABSTRACT FROM AUTHOR]

In today's world, data leakage on computer systems, Internet of Things (IoT) devices, or mobile devices carriages a significant threat due to weaker encryption or communication techniques, resulting in the loss of data items. Identifying the leakage of sensitive data during data transmission requires an appropriate technique. In the IoT environment, default permissions granted to devices often lead to data leakage. This proposed method offers data leakage security based on data sensitivity. However, classifying sensitive data is challenging due to its large volume and data transformation. The proposed technique utilizes the minimum redundancy and maximum relevancy (mRMR) technique for feature selection. It accurately detects confidential data better than existing state-of-the-art methods and can identify rephrased confidential contents using filter-based features for sequential data leak deterrence. Specifically, data leakage measurement can be achieved using DBSCAN (Density-based Spatial Clustering of Applications with Noise) to average F-statistical values measured over individual time steps and to ensure continuity between leakage data points. [ABSTRACT FROM AUTHOR]

Transaction Memory (TM) is a concurrency control abstraction that allows the programmer to specify blocks of code to be executed atomically as transactions. However, since transactional code can contain just about any operation attention must be paid to the state of shared variables at any given time. E.g., contrary to a database transaction, if a TM transaction reads a stale value it may execute dangerous operations, like attempt to divide by zero, access an illegal memory address, or enter an infinite loop. Thus serializability is insufficient, and stronger safety properties are required in TM, which regulate what values can be read, even by transactions that abort. Hence, a number of TM safety properties were developed, including opacity, and TMS1 and TMS2. However, such strong properties preclude using prerelease as a technique for optimizing TM, because they virtually forbid reading from live transactions. On the other hand, properties that do allow prerelease are either not strong enough to prevent any of the problems mentioned above (recoverability), or add additional conditions on transactions that prerelease variables that limit their applicability (elastic opacity, live opacity, virtual world consistency). This paper introduces last-use opacity and strong last-use opacity, a pair of new TM safety properties meant to be a compromise between strong properties like opacity and minimal ones like serializability. The properties eliminate all but a small class of benign inconsistent views and pose no stringent conditions on transactions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Structured query language (SQL) has emerged as one of the most used databases, serving an array of Internet-of-Things (IoTs)-enabled services including web-transactions, grid networks, industrial activity log and proactive decision systems, smart-home, financial transactions, business communication etc. With high pace increase in SQL-driven IoT applications, the threat of SQL-injection attacks (SQLIAs) at the middleware layer has increased significantly. To address such issues, machine learning-based SQLIA-prediction systems are proposed; however, majority of the existing methods are found limited in terms of intrusion detection accuracy because of their complete-reliance on structural features and inferior learning model(s). On the contrary, intruders these days intrude the system by mimicking the normal queries and hence confuses most of the classical learning-based methods. To alleviate such problems, this article emphasizes on exploiting semantic features along with the state-of-art highly robust computing environment. We proposed a robust semantic query-featured ensemble learning model for SQLIA prediction. Unlike classical (query's) template-matching or term-assessment-based methods, our proposed SQLIA-prediction model exploits latent semantic features from large SQL-queries to train an ensemble learning model that classifies each query as the normal query or the SQLIA query. Functionally, it performs preprocessing over large set of SQL-queries using count-vectorizer and stopping word removal. Subsequently, it applies Word2Vec feature extraction method over each query using continuous bag of words (CBOW) and N-skip gram (SKG) algorithms, which obtained CBOW and SKG semantic features from each SQL-query. The extracted features were processed for data resampling so as to alleviate the problem of class-imbalance and skewness. To alleviate redundant computation, two feature selection algorithms named Mann-Whitney significance predictor test and principal component analysis were applied over the resampled features. Moreover, to eliminate over-fitting and convergence problem, Min-Max normalization was performed over the selected features which were later processed for learning using a state-of-art robust heterogeneous ensemble learning model. Unlike standalone classifier-based SQLIA, the proposed learning-model employed a set of nine base classifiers designed to serve maximum voting ensemble-based prediction. The proposed ensemble-learning method classified each SQL-query as the normal-query or the SQLIA-query. Simulation results affirmed superiority of the proposed SQLIA prediction model in terms of accuracy (98%), F-Score (0.989), AUC (0.999) signifying its efficacy toward real-world SQL-driven IoT-ecosystems. [ABSTRACT FROM AUTHOR]

Individual conditions are essential in pharmacotherapy of hypertension; however, information regarding the association between anti-hypertensive drugs and co-morbid diseases as recommended in clinical practice guidelines is not well acquainted. Therefore, this study aimed to report the prevalence of prescribed antihypertensive drugs using Thailand National Health Security Office database. In 2014, we found that calcium channel blockers (CCBs), angiotensin converting enzyme inhibitors (ACEIs), and diuretics, were prescribed in 56.11%, 42.70%, and 33.58% of total patients, respectively. Similarly in 2015, CCBs and ACEIs were prescribed in 57.69% and 41.01% of total patients, whereas the 3rd most frequently prescribed was anti-adrenergics (28.55%). As recommended in the latest Thailand clinical practice guideline at that period, as a mono-therapy, patients younger than 55 years old without co-morbid diseases received either ACEIs (28.91% and 28.40%) or ARBs (6.70% and 6.33%); however, CCBs were prescribed to these patients in the highest percentage (33.25% and 40.35% in 2014 and 2015, respectively). Focusing on patients with co-morbid conditions, CCBs were prescribed at 54.23% and 55.72% in patients with diabetes mellitus and 62.53% and 62.58% in patients with chronic kidney diseases in 2014 and 2015, respectively. The percentages of patients with chronic kidney diseases who received CCBs were relatively higher than those treated with either ACEIs or ARBs, the first-line recommendation. Interestingly, we found the inappropriate combination between ACEIs and ARBs. Our finding revealed that although the anti-hypertensive drug prescription in Thailand was in line with the recommendations in clinical practice guidelines, certain problematic prescriptions remained available. [ABSTRACT FROM AUTHOR]

Graph-based databases are concerned with performance and flexibility. Most of the existing approaches used to design secure NoSQL databases are limited to the final implementation stage, and do not involve the design of security and access control issues at higher abstraction levels. Ensuring security and access control for Graph-based databases is difficult, as each approach differs significantly depending on the technology employed. In this paper, we propose the first technology-ascetic framework with which to design secure Graph-based databases. Our proposal raises the abstraction level by using ontologies to simultaneously model database and security requirements together. This is supported by the TITAN framework, which facilitates the way in which both aspects are dealt with. The great advantages of our approach are, therefore, that it: allows database designers to focus on the simultaneous protection of security and data while ignoring the implementation details; facilitates the secure design and rapid migration of security rules by deriving specific security measures for each underlying technology, and enables database designers to employ ontology reasoning in order to verify whether the security rules are consistent. We show the applicability of our proposal by applying it to a case study based on a hospital data access control. • Database designers can focus on data to be protected, ignoring the implementation. • Facilitates quick migration of security rules to each underlying database technology. • Enable checking whether the security rules are consistent through ontology reasoning. [ABSTRACT FROM AUTHOR]

Our main focus is to identify the most used methodologies in database security from the review of publications on academic journals in databases from the last six years. An evaluation of the most common databases and security methodologies today was conducted. In order to develop this a systematic review of the IEEE, ARXIV, HINDAWI and DIALNET databases was done. Then, applying the established inclusion and exclusion criteria it resulted in a total of 14 articles showing the protocols and methodologies to answer the research question posed. It was found that for the security of databases the access control methodology is applied a lot with different methods proposed as authentication systems and biometric authentications since these methods are the ones that generate a higher level of security. [ABSTRACT FROM AUTHOR]

Building on Scott's notion of identity as a key concept in early modern statehood, this paper historically contextualises and analyses the current political re-problematisation of identity in the EU. Engaging the recently adopted interoperability initiative that is set to biometrically verify and cross-validate identity records between all European border management, migration, and security databases, it argues that interoperability presents a shift from traditional modes of identity production at the border towards a digital space of identity management. Such identity management is predicated on the establishment of a biometric super-layer structure that cuts across databases without dissolving their legal foundations and introduces a new mode of 'truth' production in the form of a dedicated 'identity confirmation file' that is supposed to re-introduce a reliable baseline for the government of the Schengen area. [ABSTRACT FROM AUTHOR]

Quantum-key-distribution (QKD)-based quantum private queries (QPQs) are the most practical protocols for the symmetrically private information retrieval problem. But these protocols cannot resist joint-measurement (JM) attack. Recently, Wei et al. proposed a QPQ protocol with good performance in resisting JM attack (RJM-QPQ) (Phys. Rev. A 93, 042318 (2016)). However, due to the imperfections of ideal single-photon source, one has to consider the security of this protocol under weak coherent pulses sources (WCPS). In this paper, after analyzing the security loophole of RJM-QPQ protocol under WCPS, we propose a QPQ protocol with decoy-state method (DS-QPQ). Compared to the RJM-QPQ protocol, DS-QPQ has better performance under WCPS. It can also resist the JM attack. Furthermore, it improves database security, user privacy and efficiency. Moreover, DS-QPQ retains the good characters of QKD-based QPQs, e.g., it is loss tolerant and robust against quantum memory attack. [ABSTRACT FROM AUTHOR]

Database security focuses on protecting most organization's virtual data storage unit and confidential information from malicious threats and external attacks. To keep out data secure, we need to use a role-based access control (RBAC) approach to accurately differentiate access permissions, but SQL queries written by an authorized user have very similar characteristics and are difficult to distinguish. In this paper, we propose a method of optimizing CNN-LSTM neural networks with particle swarm optimization (PSO) to classify the roles in RBAC system. Convolutional neural network (CNN) can extract parsed SQL queries into smaller details and features through an analysis mechanism. Long short-term memory (LSTM) is also suitable for modeling the temporal information of SQL queries to recognize the context of user authorities. PSO repeatedly searches and optimizes the complex hyperparameter space of the CNN-LSTM. Our PSO-based CNN-LSTM neural networks outperform other deep learning and machine learning models in the TPC-E benchmark SQL query statement. Finally, experiments and analysis show the usefulness of PSO and identify the important SQL query features that affect user role classification. [ABSTRACT FROM AUTHOR]

Nowadays, more individuals and corporations tend to use machine learning as a service (MLaaS) in cloud computing environment. However, when enjoying the pay-as-you-go mode and flexible capacity of cloud computing, it also increases the risk of privacy leakage for sensitive data. In this paper, we aim to efficiently implement privacy-preserving MLaaS, and focus on k-means clustering over outsourced encrypted cloud databases. Previous works mainly utilize partially homomorphic encryptions, which require a great number of interactive protocols with high computation and communication costs, making them not practical in real-world applications. To better solve this problem, we propose a new secure and efficient outsourced k-means clustering (SEOKC) scheme using fully homomorphic encryption with ciphertext packing technique, which achieves parallel computation without extra cost. The proposed scheme preserves privacy in three aspects: (1) database security, (2) privacy of clustering results and (3) hiding of data access patterns. We provide formal security analysis and evaluate the performance of the proposed scheme through extensive experiments. The experiment results show that our scheme needs much less computation cost (more than three orders of magnitude lower) than the state-of-the-art schemes, and is suitable to be applied on large databases. [ABSTRACT FROM AUTHOR]

Open Journal Systems (OJS), a modern era Publishing tool for authors, reviewers and editors have gained a lot of popularity in the recent times as this software is available free for use on web and publishes journal online. While this tool empowers its user to validate, support, control, track publications, etc, at the same time its wide user base has raised few concerns about data security. This article deals with security issues that may arise from the use of this web-based journal management and publishing software by the author and also suggests measures/precautions on how to minimise the possible risk related to data security based on author experience in certain situations. For this, the author has adopted a methodology that synchronises reviewed research papers with thoughts gained by reading various blogs and documentation and doing analysis of same. With this contribution from the author, the user is expected to benefit from the implementation of suggestive guidance/approach as prescribed in this article to overcome similar issues, which may be faced by some users. The author has endeavored to express the associated security issues, recommend solutions and security steps to be followed while using the OJS in certain situations. [ABSTRACT FROM AUTHOR]

We introduce the notion of private set operations (PSO) as a symmetric-key primitive in the cloud scenario, where a client securely outsources his dataset to a cloud service provider and later privately issues queries in the form of common set operations. We define a syntax and security notion for PSO and propose a general construction that satisfies it. There are two main ingredients to our PSO scheme: an adjustable join (Adjoin) scheme (MIT-CSAIL-TR-2012-006 (2012) Cryptographic treatment of CryptDB's adjustable join. http://people.csail.mit.edu/nickolai/papers/popa-join-tr.pdf) and a tuple set (TSet) scheme (Cash, D. Jarecki, S. Jutla, C. S. Krawczyk, H. Rosu, M.-C. and Steiner, M. (2013) Highly-Scalable Searchable Symmetric Encryption With Support for Boolean Queries. 33rd Annual Cryptology Conf. , Santa Barbara, CA, August 18–22, pp. 353–373. Springer, Berlin, Heidelberg). We also propose an Adjoin construction that is substantially more efficient (in computation and storage) than the previous ones (Mironov, I. Segev, G. and Shahaf, I. (2017) Strengthening the Security of Encrypted Databases: Non-Transitive Joins. 15th Int. Conf. TCC 2017 , Baltimore, MD, USA, November 12–15, pp. 631–661. Springer, Cham) due to the hardness assumption that we rely on, while retaining the same security notion. The proposed PSO scheme can be used to perform join queries on encrypted databases without revealing the duplicate patterns in the unqueried columns, which is inherent to an Adjoin scheme. In addition, we also show that the PSO scheme can be used to perform Boolean search queries on a collection of encrypted documents. We also provide standard security proofs for our constructions and present detailed efficiency evaluation and compare them with well-known previous ones. [ABSTRACT FROM AUTHOR]

Currently, data production is as quick as possible; however, databases are collections of well-organized data that can be accessed, maintained, and updated quickly. Database systems are critical to your company because they convey data about sales transactions, product inventories, customer profiles, and marketing activities. To accomplish data manipulation and maintenance activities the Database Management System considered. Databases differ because their conclusions based on countless rules about what an invulnerable database constitutes. As a result, database protection seekers encounter difficulties in terms of a fantastic figure selection to maintain their database security. The main goal of this study is to identify the risk and how we can secure databases, encrypt sensitive data, modify system databases, and update database systems, as well as to evaluate some of the methods to handle these problems in security databases. However, because information plays such an important role in any organization, understanding the security risk and preventing it from occurring in any database system require a high level of knowledge. As a result, through this paper, all necessary information for any organization has been explained; in addition, also a new technological tool that plays an essential role in database security was discussed. [ABSTRACT FROM AUTHOR]

In order to overcome the problems of high data storage occupancy and long encryption time in traditional integrity protection methods for trusted data of IoT node, this paper proposes an integrity protection method for trusted data of IoT node based on transfer learning. Through the transfer learning algorithm, the data characteristics of the IoT node is obtained, the feature mapping function in the common characteristics of the node data is set to complete the classification of the complete data and incomplete data in the IoT nodes. The data of the IoT nodes is input into the data processing database to verify its security, eliminate the node data with low security, and integrate the security data and the complete data. On this basis, homomorphic encryption algorithm is used to encrypt the trusted data of IoT nodes, and embedded processor is added to the IoT to realize data integrity protection. The experimental results show that: after using the proposed method to protect the integrity of trusted data of IoT nodes, the data storage occupancy rate is only about 3.5%, the shortest time-consuming of trusted data encryption of IoT nodes is about 3 s, and the work efficiency is high. [ABSTRACT FROM AUTHOR]