Data Integrity

We present FastVer, a high-performance key-value store with strong data integrity guarantees. FastVer is built as an extension of FASTER, an open-source, high-performance key-value store. It offers the same key-value API as FASTER plus an additional verify() method that detects if an unauthorized attacker tampered with the database and checks whether results of all read operations are consistent with historical updates. FastVer is based on a novel approach that combines the advantages of Merkle trees and deferred memory verification. We show that this approach achieves one to two orders of magnitudes higher throughputs than traditional approaches based on either Merkle trees or memory verification. We have formally proven the correctness of our approach in a proof assistant, ensuring that verify() detects any inconsistencies, except if a collision can be found on a cryptographic hash.

The aim of this paper is to show a method that is able to detect a particular class of semantic inconsistencies in a deductive system (DS). A DS verified by this method contains a set of first order production rules, and a description logic terminology that defines the domain problem. By building an ATMS-like theory the method is able to give an specification of all the initial Fact Bases (FBs), and the rules that would have to be executed from these initial FBs to produce an inconsistency.

Despite the dominance of the service sector in the last decades, there is still a need for a strong foundation on service design and innovation. Little attention has paid on service modelling, particularly in the collaboration context. Collaboration is considered as one of solutions for surviving or sustaining the business in the high competitive atmosphere. Collaborative services require various service providers working together according to agreements between them, along with service consumers, in order to coproduce services. In this paper, we address crucial issues in collaborative services such as collaboration levels, sharing data and processes due to business interdependencies between service stakeholders. Afterward, we propose a model for Collaborative Service Modellingthe COSEMO model, which is able to cover identified issues. We also apply our proposed model to modelling an example of healthcare services in order to illustrate the relevance of our modelling approach to the matter in hand. This section addresses the important challenges in collaborative services modelling.

Rules based approaches for data quality solutions often use business rules or integrity rules for data monitoring purpose. Integrity rules are constraints on data derived from business rules into a formal form in order to allow computerization. One of challenges of these approaches is rules discovering, which is usually manually made by business experts or system analysts based on experiences. In this paper, we present our rule-based approach for data quality analyzing, in which we discuss a comprehensive method for discovering dynamic integrity rules.

The U.S. Geological Survey is evaluating potentially useful surrogate instruments and methods for inferring the physical characteristics of suspended sediments. Instruments operating on bulk acoustic, bulk and digital optic, laser, and pressure-differential technologies are being tested in riverine and laboratory settings for their usefulness to Federal agencies toward providing quantifiably reliable information on bed-material and bed-topography characteristics, and on concentrations, size distributions and transport rates of sediments in suspension and as bedload. The efficacy of suspended-sediment surrogate technologies based on bulk-optic, digital optic, laser, acoustic, and pressure-difference principles has been evaluated in Arizona, Florida, Kansas, Puerto Rico, and Washington. All but the pressure-difference technology have been shown to provide reliable data under a limited set of riverine and laboratory conditions, and pressure-difference technology tests continue at a new site in Arizona.

Data is inherently dirty and there has been a sustained effort to come up with different approaches to clean it. A large class of data repair algorithms rely on data-quality rules and integrity constraints to detect and repair the data. A well-studied class of integrity constraints is Functional Dependencies (FDs, for short) that specify dependencies among attributes in a relation. In this paper, we address three major challenges in data repairing: (1) Accuracy: Most existing techniques strive to produce repairs that minimize changes to the data. However, this process may produce incorrect combinations of attribute values (or patterns). In this work, we formalize the interaction of FD-induced patterns and select repairs that result in preserving frequent patterns found in the original data. This has the potential to yield a better repair quality both in terms of precision and recall. (2) Interpretability of repairs: Current data repair algorithms produce repairs in the form of data updates that are not necessarily understandable. This makes it hard to debug repair decisions and trace the chain of steps that produced them. To this end, we define a new formalism to declaratively express repairs that are easy for users to reason about. (3) Scalability: We propose a linear-time algorithm to compute repairs that outperforms state-of-the-art FD repairing algorithms by orders of magnitude in repair time. Our experiments using both real-world and synthetic data demonstrate that our new repair approach consistently outperforms existing techniques both in terms of repair quality and scalability.

Academic transcripts are essential documents in higher education, reflecting students' academic performance and capabilities. However, the current management of transcript data at Halu Oleo University (UHO) lacks safeguards against unauthorized alterations, compromising their authenticity. This study proposes a method using the secure hash algorithm 3 (SHA-3) and the digital signature standard (DSS) scheme to ensure the integrity of transcript data. A Python-based web module for managing transcripts and a signing program using SHA-3 and DSS were developed and implemented. This method digitally signs transcript files, ensuring that subsequent changes invalidate the current digital signature. Efficiency tests demonstrated an average signing time of 0.242 seconds, indicating a practical and efficient solution. The study's findings emphasize how SHA-3 and DSS effectively authenticate academic transcript files, preventing unauthorized modifications and safeguarding the integrity of critical educational records. This method presents a robust and efficient solution for educational institutions to strengthen the security and reliability of their academic record management systems.

Health information systems are increasingly complex, and their development is presented as a challenge for software development companies offering quality, maintainable and interoperable products. HL7 (Health level 7) International, an international non-profit organization, defines and maintains standards related to health information systems. However, the modelling languages proposed by HL7 are far removed from standard languages and widely known by software engineers. In these lines, NDT is a software development methodology that has a support tool called NDT-Suite and is based, on the one hand, on the paradigm of model-driven engineering and, on the other hand, in UML that is a widely recognized standard language. This paper proposes an extension of the NDT methodology called MoDHE (Model Driven Health Engineering) to offer software engineers a methodology capable of modelling health information systems conforming to HL7 using UML domain models.

This research work proposes a method for managing, securing, and validating the health data distribution records using a genetic-based hashing algorithm in a decentralized environment. The reason behind choosing blockchain is to secure the transaction of health data and protect the data from manipulated fraudulent movement and corruption by the contributor of the chain or any individual. The path of this technology approach provides an efficient surveillance measure such as transparency of record, fraud immunity, and protection to tempering and sustaining the order of data. In medical research, this paper provides a genetic-based hashing algorithm for data security which has lower computational complexity, low space coverage, higher security and integrity, and a high avalanche effect. The simulation will show data records' validity, immunity, and integrity. The technique modified in this secure decentralized network is a cryptographic hashing algorithm for 512 bits. In this stud...

Completion of this thesis would not have been possible without the support and contribution of many people. It is a great honor for me to thank some of those many, to whom I owe my deepest gratitude. I would like to express my deepest appreciation to my supervisor, Dr. Sanjay Srinivasan for his excellent guidance, caring, patience, and providing me with an excellent atmosphere for doing research. He continually and convincingly conveyed a spirit of adventure in regard to research, and courage and versatility to battle challenging problems that I face in life. His scientific intuition, genius, invaluable insights, and continuous involvement were indispensable in developing my academic merits. Additional gratitude goes to Dr. Mrinal Sen for agreeing to be my secondary reader on such short notice. It gives me great pleasure in acknowledging the support of Dr. Kamy Sepehrnoori for helping me get through difficult times, and for all the emotional and technical guidance he provided during my life in Austin. I would also like to acknowledge the staff of the Department of Petroleum and

An educational record contains information directly related to an individuals’ academic
performance which needs to be shared as a part of the educational system among various stakeholders
consisting of students, schools, companies, faculties and any third-party organization which needs to
authenticate students’ credentials. Secure sharing of these credentials by ensuring privacy and authenticity is
a tedious and complex task prone to error because of the traditional client server model used. Our aim in this
paper is to leverage Blockchain as a solution to build a prototype for making the sharing of students’
credentials authentic, secure, non-repudiable, tamper-proof and ensure privacy. The proposed architecture
uses IPFS as an Off-Chain Storage, Proxy Re-Encryption with Blockchain architecture for increasing privacy
and removing unwanted storage overheads and using Proof of Authority Consensus speeding up the block
validation process. Smart Contracts are developed for Access Control and storing data into Blockchain. The
viability and execution of the prototype is tested and analyzed on Ethereum based local Blockchain
simulation platform.

To answer user queries, a data integration system employs a set of semantic mappings between the mediated schema and the schemas of data sources. In dynamic environments sources often undergo changes that invalidate the mappings. Hence, once the system is deployed, the administrator must monitor it over time, to detect and repair broken mappings. Today such continuous monitoring is extremely labor intensive, and poses a key bottleneck to the widespread deployment of data integration systems in practice. We describe Maveric, an automatic solution to detecting broken mappings. At the heart of Maveric is a set of computationally inexpensive modules called sensors, which capture salient characteristics of data sources (e.g., value distributions, HTML layout properties). We describe how Maveric trains and deploys the sensors to detect broken mappings. Next we develop three novel improvements: perturbation (i.e., injecting artificial changes into the sources) and multi-source training to improve detection accuracy, and filtering to further reduce the number of false alarms. Experiments over 114 real-world sources in six domains demonstrate the effectiveness of our sensor-based approach over existing solutions, as well as the utility of our improvements.

Molecular biology offers a large, complex and volatile domain that tests knowledge representation techniques to the limit of their fidelity, precision, expressivity and adaptability. The discipline of molecular biology and bioinformatics relies greatly on the use of community knowledge, rather than laws and axioms, to further understanding, and knowledge generation. This knowledge has traditionally been kept as natural language. Given the exponential growth of already large quantities of data and associated knowledge, this is an unsustainable form of representation. This knowledge needs to be stored in a computationally amenable form and ontologies offer a mechanism for creating a shared understanding of a community for both humans and computers. Ontologies have been built and used for many domains and this chapter explores their role within bioinformatics. Structured classifications have a long history in biology; not least in the Linnean description of species. The explicit use of ontologies, however, is more recent. This chapter provides a survey of the need for ontologies; the nature of the domain and the knowledge tasks involved; and then an overview of ontology work in the discipline. The widest use of ontologies within biology is for conceptual annotationa representation of stored knowledge more computationally amenable than natural language. An ontology also offers a means to create the illusion of a common query interface over diverse, distributed information sources -here an ontology creates a shared understanding for the user and also a means to computationally reconcile heterogeneities between the resources. Ontologies also provide a means for a schema definition suitable for the complexity and precision required for biology's knowledge bases. Coming right up to date, bioinformatics is well set as an exemplar of the Semantic Web, offering both web accessible content and services conceptually marked up as a means for computational exploitation of its resources -this theme is explored through the my GRID services ontology. Ontologies in bioinformatics cover a wide range of usages and representation styles. Bioinformatics offers an exciting application area in which the community can see a real need for ontology based technology to work and deliver its promise.

The complex physical processes controlling ceiling and visibility (for example, the formation, evolution and motion of low cloud, precipitation and fog) and the diverse seasonal and geographic influences that modulate these controls across the continental U.S. and Alaska yield an extremely difficult analysis and forecast problem. This same phenomenology significantly impacts the safety and continuity of aviation operations, making VFR (visual flight rules) flight into impacted conditions associated with IFR (instrument flight rules) the leading cause among weather-related aviation accidents in the U.S.

The development of machine learning (ML) technologies provide a new development direction for cryptanalysis. Several ML research in the field of cryptanalysis was carried out to identify the cryptographic algorithm used, find out the secret key, and even recover the secret message The first objective of this study is to see how much influence optimization and activation function have on the multi-layer perceptron (MLP) model in performing cryptanalysis. The second research objective, which is to compare the performance of cryptanalysis in recovering keys and the plaintext. Several experiments have been carried out, the observed parameters found that the use of the rectified linear unit (ReLU) activation function and the ADAM optimizer improves the performance of deep learning (DL)-based cryptanalysis as evidenced by a significantly smaller error rate. DL-based cryptanalysis works more effectively in recovering keys than recovering plaintext. DL-based cryptanalysis managed to recover the keys with an average loss of 0.007, an average of 49 epochs, and an average time of 0.178 minutes.

The Italian National Statistics Institute is currently integrating its various legacy spatio-temporal data collections. The SIT-IN project has delivered a first release, whose development relied on web and relational technologies to manage data heterogeneity. The final system provides users with many different classes of functions with which to analyse and visualise territorial data. It can be viewed as a spatiotemporal data warehouse, where space and time are the main access dimensions to statistical data, but also where the ...

Global positioning systems first became available for private use in 1995. Since the introduction of NAVSTAR-GPS (Navigation System with Time and Ranging -Global Positioning System) and GLONASS (Globaluaya Navigatsionnaya Sputnikovaya Sistema, Global Navigation Satellite System), such systems have quickly become indispensable in a wide range of applications. Above all, GPS is used today by a large user community. Artificial augmentations can be used to improve the performance of the systems in terms of accuracy, availability and integrity. In space-based augmentations, differential correction data, integrity information and additional ranging signals are transmitted from geostationary satellites. In ground-based augmentations, a reference station (based on a geo-referenced position) compares the position solution (calculated by the SatNav System) with the real coordinates. In that case, the correction data is transmitted by a telemetric system to roving receivers near the reference station. Both augmentation techniques are based on the generic satellite navigation systems GPS or GLONASS, and are dependent on the availability of the source system. To overcome the dependency of the civil European user community upon foreign military systems, an initiative to build up an autonomous European Satellite Navigation System with an own-space segment was initiated. Since 1999, the realisation of this project has been on the way, and the employment of the new system, named Galileo, is currently expected in 2008.

Peer-to-peer systems have considerably evolved since their original conception, in the 90's. The idea of distributing files using the user's terminal as a relay has now been widely extended to embrace virtually any form of resource (e.g., computational and storage resources), data (e.g. files and real-time streams) and service (e.g., IP telephony, IP TV, collaboration). Robustness, resilience and autonomic management are the key evolutionary step which makes them best fitted in dynamic, large-scale, decentralized environments. Peer-to-Peer systems provide network applications the opportunity to leap over the boundaries of the standardized and best effort protocols and the constraints of administrative domains. We take here the opportunity to warmly thank all the members of the AP2PS 2011 Technical Program Committee, as well as the numerous reviewers. The creation of such a broad and high quality conference program would not have been possible without their involvement. We al...

Web services are pivotal in contemporary software development, connecting diverse systems over standardized protocols and enabling data sharing across platforms. From the early days of the Simple Object Access Protocol (SOAP) to the widespread adoption of Representational State Transfer (REST), these foundational architectures have influenced how developers build and deploy distributed applications. In recent years, the rise of microservices has further revolutionized application design by breaking monolithic systems into smaller, loosely coupled services that can scale independently. This article explores the historical progression of these foundational web service architectures, explaining core concepts, highlights, and comparisons. It also examines their practical applications, including considerations for heavily regulated domains such as healthcare. By discussing both established paradigms and emerging approaches, the article provides insight into how these foundational ideas continue to guide modern web service innovation.

This paper studies the problem of increasing the efficiency in controlling burst errors that are caused by external noise in the context of digital data transmission. For this purpose, the utilization of special weighted check sum modification has been proposed. Indeed, an algorithm for burst error control is presented and its use is illustrated via the presentation of an example in order to show that the technique proposed in this paper ensures superior data transmission error control effectiveness compared with already known codes.

Abstract. Exploiting the rich traces of users' Web interaction promises to enable cross-application user modeling techniques, which is in particular interesting for applications that have a small user population or that are used infrequently. In this paper we present a framework for the effective interchange of user profiles. In addition to derivation rules for user profile reasoning, the framework employs flexible mash-ups of RSS-based user data streams for combining heterogeneous user data in a Web 2.0 environment. 1

Data exchange and interoperability between clinical information systems represent a crucial issue in the context of pati ent record data collection. An XML representation schema adapted to end-stage renal disease (ESRD) patients was developed and successfully tested against patient data in the dedicated Multi-Source Information System (MSIS) active file (more than 16,000 patient records). The ESRD-XML-Schema is organized into Schema

Pharmaceutical manufacturing relies on robust Quality Assurance (QA) and Quality Control (QC) systems to ensure product safety, efficacy, and compliance with stringent regulations. This paper explores current trends, challenges, and future directions in QA/QC, highlighting advancements in automation, data analytics, continuous manufacturing, and regulatory harmonization. It also delves into critical challenges, including regulatory compliance, data integrity, supply chain complexities, and workforce shortages. To address these issues, the industry must adopt innovative technologies, robust risk management strategies, and sustainable practices while fostering a skilled workforce. These efforts are essential for maintaining high-quality standards and ensuring patient safety in a rapidly evolving landscape.

The Internet of Medical Things (IoMT) is revolutionizing healthcare through real-time monitoring and personalized interventions. However, its rapid adoption raises urgent ethical and regulatory challenges, particularly concerning data privacy and cybersecurity. This research critically examines vulnerabilities within IoMT systems and evaluates current global regulatory gaps. It presents a secure proof-of-concept diabetic monitoring system that aligns with privacy-by-design principles, demonstrating the feasibility of ethical implementation. Using empirical evidence, regulatory analysis, and cost-benefit evaluation, the study argues for a harmonized regulatory framework that prioritizes ethical compliance and system security.

NoSQL stores are emerging as an efficient alternative to relational database management systems in the context of big data. Many actors in this domain consider that to gain a wider adoption, several extensions have to be integrated. Some of them focus on the ways of proposing more schemas, supporting adapted declarative query languages and providing integrity constraints in order to control data consistency and enhance data quality. The authors consider that these issues can be dealt with in the context of Ontology Based Data Access (OBDA). OBDA is a new data management paradigm that exploits the semantic knowledge represented in ontologies when querying data stored in a database. They provide a proof of concept of OBDA's ability to tackle these three issues in a social application related to the medical domain.

Real-world data (RWD) and real-world evidence (RWE) are now central to healthcare decision-making, supporting regulatory submissions, health technology assessments (HTA), and scientific communication. Yet patients whose data fuel these processes rarely see transparency or benefit when their information is commercialised. Governance frameworks from the European Medicines Agency (EMA), U.S. Food and Drug Administration (FDA), and other bodies emphasise methodological rigor and transparency, but they seldom address fairness, reciprocity, or perceptions of legitimacy from a patient perspective.
This commentary argues that evidence integrity must extend beyond technical standards to encompass ethical stewardship. Drawing on case examples from the UK, EU, and North America, I show how opacity in consent processes, selective disclosure of data use, and absence of benefit-sharing widen the trust gap. Patients contribute information under the assumption it will improve care, not simply generate commercial value or institutional advantage.
To address this, I propose five pragmatic safeguards: (i) transparent, plain-language consent; (ii) mandatory disclosure of monetisation models; (iii) governance boards with patient representation; (iv) reinvestment of commercial gains into patient support, public health, and digital tools; and (v) mandatory registration of non-interventional studies in public registries. Together, these measures extend evidence integrity to include fairness, reciprocity, and legitimacy. The future of RWE depends not only on scientific validity but also on whether patients trust that their data are handled responsibly and ethically. Perhaps it is time to move beyond passive consent and towards a new call for accountability—captured in a simple but powerful reminder: “That is MY DATA.”

The annual Hajj presents diversified negative experiences to millions of pilgrims worldwide. The negative experiences and recommendations to overcome them as per pilgrims' feedback are yet to be analyzed from an aggregated perspective in the literature, which we do in this paper. To do so, first, we perform a large-scale survey (n=988) using our comprehensive questionnaire. Then, we perform both quantitative (e.g., clustering) and qualitative (e.g., thematic) analyses on the survey data. Our quantitative analysis reveals up to seven clusters of negative experiences. Further, going beyond the quantitative analysis, our qualitative analysis reveals 21 types of negative experiences, 20 types of recommendations, and nine themes connecting the negative experiences and recommendations. Accordingly, we reveal associations among the negative experiences and recommendations based on the themes in thematic analysis and present the associations through a tripartite graph. However, we have some limitations in this study, such as fewer female and young participants. In future, we plan to collect more responses from female and young participants and extend our work by analyzing linkages in the tripartite graph by augmenting the edges within the graph with appropriate weights. Overall, the findings of this study are expected to facilitate the prioritization of tasks for the management personnel in charge of the Hajj pilgrimage.

Global positioning systems first became available for private use in 1995. Since the introduction of NAVSTAR-GPS (Navigation System with Time and Ranging -Global Positioning System) and GLONASS (Globaluaya Navigatsionnaya Sputnikovaya Sistema, Global Navigation Satellite System), such systems have quickly become indispensable in a wide range of applications. Above all, GPS is used today by a large user community. Artificial augmentations can be used to improve the performance of the systems in terms of accuracy, availability and integrity. In space-based augmentations, differential correction data, integrity information and additional ranging signals are transmitted from geostationary satellites. In ground-based augmentations, a reference station (based on a geo-referenced position) compares the position solution (calculated by the SatNav System) with the real coordinates. In that case, the correction data is transmitted by a telemetric system to roving receivers near the reference station. Both augmentation techniques are based on the generic satellite navigation systems GPS or GLONASS, and are dependent on the availability of the source system. To overcome the dependency of the civil European user community upon foreign military systems, an initiative to build up an autonomous European Satellite Navigation System with an own-space segment was initiated. Since 1999, the realisation of this project has been on the way, and the employment of the new system, named Galileo, is currently expected in 2008.

This paper explores the integration of blockchain technology into healthcare systems to enhance cybersecurity and safeguard sensitive patient data. As healthcare organizations increasingly digitize their operations, they become vulnerable to cyber threats, data breaches, and unauthorized access to personal health information. Traditional security measures often fall short in providing comprehensive protection against sophisticated attacks. This study presents blockchain as a robust solution, leveraging its decentralized, immutable, and transparent nature to create a secure environment for health data management. By utilizing smart contracts, healthcare providers can automate and enforce data access policies while ensuring accountability and traceability. Furthermore, blockchain's ability to enable secure sharing of patient data among authorized entities facilitates interoperability and improves the overall efficiency of healthcare delivery. The paper also discusses the potential challenges of implementing blockchain in healthcare, including scalability, regulatory compliance, and integration with existing systems. Through a detailed analysis, this research aims to provide insights into how blockchain can be effectively utilized to bolster cybersecurity in healthcare systems, ultimately leading to improved patient trust, enhanced data integrity, and better health outcomes. The findings underscore the need for further exploration and adoption of blockchain solutions to address the evolving cybersecurity landscape in the healthcare sector.

There is a growing need for strong methods to guarantee the accuracy and reliability of data due to the widespread use of next-generation AI in automated processes. This research delves into new approaches to rethink AI system quality checks, with a focus on context-aware, adaptable, and dynamic validation. Modern artificial intelligence ecosystems are notoriously difficult for traditional data integrity frameworks to manage due to the sheer volume and variety of data streams and continuous learning paradigms used therein. A proactive and scalable quality assurance methodology is proposed by this study by combining state-of-the-art methods including feedback loops, explainable AI, and anomaly detection. Research shows that using these methods greatly improves AI-driven processes in terms of accuracy and dependability while decreasing the likelihood of bias, mistakes, and inefficiencies. Findings from this research highlight the need of continuously improving quality assurance procedures for sustaining credibility and efficiency in the age of intelligent automation. This paper delves into the changing landscape of quality assurance in AI-driven processes, with a focus on how automated workflows must prioritise data integrity. With their reliance on varied, high-volume information and complicated algorithms, next-generation AI systems are dynamic and complex, making traditional quality checks inadequate. In order to guarantee strong data integrity, this study suggests a new AI quality assurance system that combines adaptive mistake detection, predictive analytics, and sophisticated validation techniques. The framework reimagines quality standards in AI operations by using state-of-the-art technologies such as blockchain for traceability and federated learning for decentralised validation. There are noticeable gains in efficiency, accuracy of decisions, and reduction of errors in empirical assessments. The results highlight the need to reconsider quality standards in order to build trustworthy and reliable AI ecosystems, which will allow for their ethical and scalable implementation. Organisations striving to align AI systems with strict quality and integrity requirements in increasingly automated settings might look to our work as a benchmark.

Formal methods can bring many advantages to software practitioners and their adoption has been often advocated. In recent years usage of formal techniques certainly increased, nevertheless there is still ample room for further adoption and a wide gap exists between formal methods experts, with knowledge on formal tools, and domain experts, which possess the knowledge on the problems to solve and on users requirements. In this paper we introduce a user-friendly approach for Business Process assessment based on formal verification techniques. Starting from a semi-formal notation, well understood and largely used by domain experts, we provide a denotational mapping to a formal specification in the form of a process algebra. This transformation makes possible formal and automatic verification of desired quality requirements. The approach has been already applied, with encouraging results, in the e-government domain to verify the quality of Business Processes related to the delivery of e-Government Digital Services. Moreover the approach is supported by a plug-in for the Eclipse platform permitting to have an integrated environment in which to design the Business Process model and to assess its quality.

Web form spamming is a growing cybersecurity threat that disrupts digital services and compromises data integrity. Traditional defenses like CAPTCHA are increasingly ineffective against sophisticated bots. This study proposes a blockchain-based framework for managing network intrusion caused by web form spam. By leveraging smart contracts and micro-incentivization through token deposits, the system validates legitimate submissions and discourages malicious activity. A comparative experiment between CAPTCHA-only and blockchain-enabled systems demonstrates significant improvement in spam detection accuracy and user experience, with minor latency trade-offs. The results highlight blockchain’s potential as a secure, transparent, and decentralized solution for web form spam prevention.

Recent studies have indicated that companies are increasingly experiencing Data Quality (DQ) related problems as more and more complex data are being collected. In order to address such problems, literature suggests the implementation of a Total Data Quality Management Program (TDQM) that should consist of the following phases: data quality definition, measurement, analysis and improvement. DQ is often defined as "fitness for use". Although "fitness for use" captures the essence of quality, it is difficult to measure DQ using this broad definition. Thus, it has long been acknowledged that the quality of data is best described or analyzed via multiple attributes or dimensions. Yet, despite broad discussion in the DQ literature, there is no one definite set and exact definition of DQ dimensions because DQ is context dependent. Therefore, DQ dimensions should be identified and defined in relation to tasks to achieve a suitable level of DQ. This work identifies the important DQ dimensions for evaluating the quality of the data for credit risk assessment. It also explores the key DQ challenges and causes of DQ problems in financial institutions. Findings of a statistical analysis of an empirical study identify nine important DQ dimensions including accuracy and security for assessing the quality of the data in credit risk databases.

Digital watermarking has been in multimedia data use over the past years. Recently it has become applicable in relational database system not only to secure copyright ownership but also to ensure data contents integrity. Further, it is used in locating tampered and modified places. However, the watermarkingrelational database has its own requirements, challenges, attacks and limitations. This paper, surveys recent database watermarking techniques focusing on the importance of watermarking relational database, the difference between watermarking relational database and multimedia objects, the issues in watermarking relational database, type of attacks on watermarked database, classifications, distortion introduced and the embedded information. The comparative study shows that watermarking relational database can be an effective tool for copyright protection, tampered detection, and hacker tracing while maintaining the integrity of data contents. In addition, this study explores the c...

Cloud storage provides users to easily store their data and enjoy the good quality of cloud applications which is need not install in any local hardware and software system. such a service is also gives users control of their outsourced data, which also provides control to the security problems regarding the correctness of the data storage in the cloud. The main goal of cloud computing the data protection, secure and the process data stored under the property of users. As the data which is stored at the remote server how the cloud users will get the confirmation about data which are stored. That’s why cloud storage should have some technique and mechanism which will specify correctness of data storage and integrity of data stored on cloud. For that users can resort to a third-party auditor (TPA) which is to check the integrity of outsourced data ,TPA should be able to efficiently audit the data storage on cloud . To introduce an effective and secure TPA, the auditing process should ...

There is a growing need for strong methods to guarantee the accuracy and reliability of data due to the widespread use of next-generation AI in automated processes. This research delves into new approaches to rethink AI system quality checks, with a focus on context-aware, adaptable, and dynamic validation. Modern artificial intelligence ecosystems are notoriously difficult for traditional data integrity frameworks to manage due to the sheer volume and variety of data streams and continuous learning paradigms used therein. A proactive and scalable quality assurance methodology is proposed by this study by combining state-of-the-art methods including feedback loops, explainable AI, and anomaly detection. Research shows that using these methods greatly improves AI-driven processes in terms of accuracy and dependability while decreasing the likelihood of bias, mistakes, and inefficiencies. Findings from this research highlight the need of continuously improving quality assurance procedures for sustaining credibility and efficiency in the age of intelligent automation. This paper delves into the changing landscape of quality assurance in AI-driven processes, with a focus on how automated workflows must prioritise data integrity. With their reliance on varied, high-volume information and complicated algorithms, next-generation AI systems are dynamic and complex, making traditional quality checks inadequate. In order to guarantee strong data integrity, this study suggests a new AI quality assurance system that combines adaptive mistake detection, predictive analytics, and sophisticated validation techniques. The framework reimagines quality standards in AI operations by using state-of-the-art technologies such as blockchain for traceability and federated learning for decentralised validation. There are noticeable gains in efficiency, accuracy of decisions, and reduction of errors in empirical assessments. The results highlight the need to reconsider quality standards in order to build trustworthy and reliable AI ecosystems, which will allow for their ethical and scalable implementation. Organisations striving to align AI systems with strict quality and integrity requirements in increasingly automated settings might look to our work as a benchmark.

Many applications that make use of sensor networks require secure communication. Because asymmetric-key solutions are difficult to implement in such a resource-constrained environment, symmetric-key methods coupled with a priori key distribution schemes have been proposed to achieve the goals of data secrecy and integrity. These approaches typically assume that all nodes are similar in terms of capabilities and, hence, deploy the same number of keys in all sensors in a network to provide the aforementioned protections. In this paper, we demonstrate that a probabilistic unbalanced distribution of keys throughout the network that leverages the existence of a small percentage of more capable sensor nodes can not only provide an equal level of security, but also reduce the consequences of node compromise. To fully characterize the effects of the unbalanced key management system, we design, implement, and measure the performance of a complementary suite of key establishment protocols known as LIGER. Using their predeployed keys, nodes operating in isolation from external networks can securely and efficiently establish keys with each other. Should resources such as a backhaul link to a key distribution center (KDC) become available, networks implementing LIGER automatically incorporate and benefit from such facilities. Detailed experiments demonstrate that the unbalanced distribution in combination with the multimodal LIGER suite offers a robust and practical solution to the security needs in sensor networks.

This paper proposes a system which can be used for the early indication of fungus affected plants in an Agricultural Field. As the yield of crop depends on the healthy growth of the plants, the status of the plants needs to be monitored continuously. The optimization in terms of plant growth requires knowledge of pests which affects the yield of the crop and also the amount of pesticides to be used at appropriate time, which can be done with the help of this proposed system. It uses a real-time sensor data collection with the help of different types of sensors like CMOS Image sensors, camera sensors, Laser sensors etc to know about the day-to-day updates in an Agricultural Field. The real-time data can be processed and analyzed via Internet for monitoring the fields at any time, from anywhere in the World with the help of PC or internet-enabled mobile phone which is obtained from the WSVN.

To use multi-station data to measure charge transport for IC and CG flashes independent from a Lightning Mapping Array (LMA) ⋄ To use multi-station ∆E data constrained by LMA data to see time and space dependence of charge transport along different lightning channels in a single flash. ⋄ To observe and detail positive breakdowns that are poorly visible to radio techniques Instrument Capabilities Dynamic Range: Twenty million to one Measurement Range: Useful measurements on lightning flashes up to 150 km away Sample Rate: 50 kS/sec sustained on 4 channels at 16-bits/sample (24 MBytes/minute) Power: 17 Watt/station, Solar-powered Sites: Six stations currently operational. Twelve are planned. Network: Wireless 802.11g server separate from instrument. Allows remote control of instrument power Software Post-Trigger: Continuous data collection in storms. Fair-weather periods used to discard uninteresting data. Software: Instruments run Debian Linux, C, and Python allowing analysis in the field, remote data collection, rapid reporting of decimated data.

Blockchain, on the other hand, is a groundbreaking technology that provides a distributed and Decentralized environment in which nodes in a list of networks can connect to each other without the need for a central authority. It has the potential to overcome the limits of Electronic Health Record (EHR) management and create a more secure, Decentralized, and safer environment for exchanging EHR data. Further, blockchain is a distributed ledger on which data can be stored and shared in a cryptographically secure, validated, and mutually agreed-upon manner across all mining nodes. The blockchain stores data with a high level of integrity and robustness, and it cannot be altered. Electronic health records (EHRs) are digitally saved health records that provide information about a person’s health. EHRs are generally shared among healthcare stakeholders, and thus are susceptible to power failures, data misuse, a lack of privacy, security, and an audit trail, among other problems. The aim of our proposed framework is firstly to implement blockchain technology for EHR and secondly to provide secure storage of electronic records by defining granular access rules for the users of the proposed framework. Moreover, this framework also discusses the scalability problem faced by the blockchain technology in general via use of off-chain storage of the records. This framework provides the EHR system with the benefits of having a scalable, secure and integral blockchain-based solution.

Abstractt-Achieving semantic interoperability is a current challenge in the field of data integration in order to bridge semantic conflicts occurring when the participating sources and receivers use different or implicit data assumptions. Providing a framework that automatically detects and resolves semantic conflicts is considered as a daunting task for many reasons, it should preserve the local autonomy of the integrated sources, as well as provides a standard query language for accessing the integrated data on a global basis. Many existing traditional and ontology-based approaches have tried to achieve semantic interoperability, but they have certain drawbacks that make them inappropriate for integrating data from a large number of participating sources. We propose semantic conflicts reconciliation (SCR) framework, it is ontology-based system in which all data semantics explicitly described in the knowledge representation phase and automatically taken into account through the interpretation mediation service phase, so conflicts detected and resolved automatically at the query time.

The advent of cloud computing has transformed the management and storage of data by providing scalable and flexible services. However, it has also raised critical security concerns related to data confidentiality, authentication, and integrity. Traditional Public Key Infrastructure (PKI) methods, which rely heavily on certificates, are often unsuitable due to their complexity and overhead. Identity-Based Signature (IBS) schemes offer a streamlined alternative by allowing users' public keys to be generated directly from their identity attributes, reducing the burden of certificate management. This paper explores the deployment of IBS schemes to ensure secure data transmission within cloud computing environments. It details the system design, elaborates on the processes of signature creation and verification, conducts a security evaluation, and assesses the scheme's performance in terms of computational and communication efficiency. Additionally, it highlights existing challenges such as the key escrow issue and trusted authority vulnerabilities, suggesting potential future enhancements for more resilient identity-based cryptographic solutions in cloud ecosystems.

Data Grids allow for seeing heterogeneous, distributed, and dynamic informational resources as if they were a uniform, stable, secure, and reliable database. According to this view, current proposals for data integration on Grids are based on the notion of global schema built over a collection of autonomous information sources. On the other hand, in dynamic and distributed environments, such a hierarchical and centralized architecture is not well suited for effective information integration. Peer-to-peer data integration aims at overcoming these drawbacks by modeling autonomous information systems as peers, and establishing mappings among peers without resorting to any hierarchical structure. In this paper, we present Hyper, a joint research initiative of Università di Roma "La Sapienza" and IBM Italia, which aims at developing principles and techniques for peer-to-peer data integration on a Grid infrastructure. The main contributions presented are a semantic characterization of P2P data integration, the deployment of our P2P framework on a Grid architecture, and the design of a query answering algorithm that is coherent both with the semantics and with the Grid infrastructure.

Model-based language specification has applications in the implementation of language processors, the design of domain-specific languages, model-driven software development, data integration, text mining, natural language processing, and corpus-based induction of models. Model-based language specification decouples language design from language processing and, unlike traditional grammar-driven approaches, which constrain language designers to specific kinds of grammars, it needs general parser generators able to deal with ambiguities. In this paper, we propose Fence, an efficient bottom-up parsing algorithm with lexical and syntactic ambiguity support that enables the use of model-based language specification in practice.

In today's digital landscape, data integrity and authenticity are critical to secure communication, financial transactions, and information exchange. Digital signatures, which rely on asymmetric cryptography, provide a robust mechanism for ensuring that data has not been tampered with while also authenticating the sender's identity. This paper explores the fundamental role of digital signatures in maintaining data integrity, reviews existing cryptographic algorithms, analyzes practical applications across industries, and highlights challenges such as key management and algorithmic vulnerabilities. The findings underscore that while digital signatures significantly enhance security, their effectiveness depends on strong encryption practices, secure key distribution, and ongoing algorithmic advancements.