Context. It is very common today that e-commerce systems store sensitiveclient information. The database administrators of these typesof systems have access to this sensitive client information and are ableto manipulate it. Therefore, data integrity is of core importance inthese systems and methods to detect fraudulent behavior need to beimplemented. Objectives. The objective of this thesis is to implement and evaluatethe features and performance impact of different methods for achievingdata integrity in a database, Oracle to be more exact.Methods. Five methods for achieving data integrity were tested.The methods were tested in a controlled environment. Three of themwas tested and performance evaluated by a tool emulating a real lifee-commerce scenario. The focus of this thesis is to evaluate the performanceimpact and the fraud detection ability of the implementedmethods. Results. This paper evaluates traditional Digital signature, Linkedtimestamping applied to a Merkle hash tree and Auditing performanceimpact and feature impact wise. Two more methods were implementedand tested in a controlled environment, Merkle hash tree and Digitalwatermarking. We showed results from the empirical analysis, dataverification and transaction performance. In our evaluation we provedour hypothesis that traditional Digital signature is faster than Linkedtimestamping. Conclusions. In this thesis we conclude that when choosing a dataintegrity method to implement it is of great importance to know whichtype of operation is more frequently used. Our experiments show thatthe Digital signature method performed better than Linked timestampingand Auditing. Our experiments did also conclude that applicationof Digital signature, Linked timestamping and Auditing decreasedthe performance by 4%, 12% and 27% respectively, which is arelatively small price to pay for data integrity.

A research report submitted to the Faculty of Science, University of the Witwatersrand, for the degree of Master of Science by Coursework and Research Report. / Researchers and data analysts often encounter a problem when analysing data with missing values. Methods for imputing continuous data are well developed in the literature. However, methods for imputing categorical data are not well established. This research report focuses on categorical data imputation using non-parametric and semi-parametric methods. The aims of the study are to compare different imputation methods for categorical data and to assess the quality of the imputation. Three imputation methods are compared namely; multiple imputation, hot deck imputation and random forest imputation. Missing data are created on a complete data set using the missing completely at random mechanism. The imputed data sets are compared with the original complete data set, and the imputed values which are the same as the values in the original data set are counted. The analysis revealed that the hot deck imputation method is more precise, compared to random forest and multiple imputation methods. Logistic regression is fitted on the imputed data sets and the original data set and the resulting models are compared. The analysis shows that the multiple imputation method affects the model fit of the logistic regression negatively.

La tolérance et la gestion des fautes dans les grilles de données/calcul est d’une importance capitale. En effet, comme dans tout autre système distribué, les composants d’une grille sont susceptibles de tomber en panne à tout moment. Mais le risque de panne croît avec la taille du système, et est donc plus exacerbé dans un système de grille. En plus, tout en essayant de mettre à profit les ressources offertes par la grille, les applications tournant sur celle-ci sont de plus en plus complexes (ex. impliquent des interactions complexes, prennent des jours d’exécution), ce qui les rend plus vulnérables aux fautes. Le plus difficile dans la gestion des fautes dans une grille, c’est qu’il est difficile de savoir si une faute qui survient sur une entité de la grille est induite malicieusement ou accidentellement. Dans notre travail de thèse, nous utilisons le terme faute, au sens large, pour faire référence à tout étant inattendu qui survient sur tout composant de la grille. Certains de ces états provoquent des comportements aussi inattendus et perceptibles au niveau de la grille tandis que d’autres passent inaperçues. De plus, certaines de ces fautes sont le résultat d’une action malveillante alors que d’autres surviennent accidentellement ou instantanément. Dans ce travail de thèse, nous avons traité le cas de ces fautes induites malicieusement, et qui généralement passent inaperçues. Nous avons considéré en particulier le problème de la confidentialité et de l’intégrité des données stockées à long-terme sur la grille. L’étude de la confidentialité des données a été faite en deux temps dont la première partie concerne la confidentialité des données actives. Dans cette partie, nous avons considéré une application liée à la recherche des similitudes d’une séquence d’ADN dans une base de données contenant des séquences d’ADN et stockée sur la grille. Pour cela, nous avons proposé une méthode qui permet d’effectuer la comparaison sur un composant distant, mais tout en gardant confidentielle la séquence qui fait l’objet de la comparaison. Concernant les données passives, nous avons proposé une méthode de partage des données confidentielles et chiffrés sur la grille. En rapport avec l’intégrité des données, nous avons considéré le cas des données anonymes dans le cadre de l’intégrité des données passives. Pour les données actives, nous avons considéré le problème de la corruption des jobs exécutés sur la grille. Pour chacune des cas, nous avons proposé des mécanismes permettant de vérifier l’authenticité des données utilisées ou produites par ces applications.

In the current era of digital world, the amount of sensitive data produced by many organizations is outpacing their storage ability. The management of such huge amount of data is quite expensive due to the requirements of high storage capacity and qualified personnel. Storage-as-a-Service (SaaS) offered by cloud service providers (CSPs) is a paid facility that enables organizations to outsource their data to be stored on remote servers. Thus, SaaS reduces the maintenance cost and mitigates the burden of large local data storage at the organization's end. For an increased level of scalability, availability and durability, some customers may want their data to be replicated on multiple servers across multiple data centers. The more copies the CSP is asked to store, the more fees the customers are charged. Therefore, customers need to have a strong guarantee that the CSP is storing all data copies that are agreed upon in the service contract, and these copies remain intact. In this thesis we address the problem of creating multiple copies of a data file and verifying those copies stored on untrusted cloud servers. We propose a pairing-based provable multi-copy data possession (PB-PMDP) scheme, which provides an evidence that all outsourced copies are actually stored and remain intact. Moreover, it allows authorized users (i.e., those who have the right to access the owner's file) to seamlessly access the file copies stored by the CSP, and supports public verifiability. We then direct our study to the dynamic behavior of outsourced data, where the data owner is capable of not only archiving and accessing the data copies stored by the CSP, but also updating and scaling (using block operations: modification, insertion, deletion, and append) these copies on the remote servers. We propose a new map-based provable multi-copy dynamic data possession (MB-PMDDP) scheme that verifies the intactness and consistency of outsourced dynamic multiple data copies. To the best of our knowledge, the proposed scheme is the first to verify the integrity of multiple copies of dynamic data over untrusted cloud servers. As a complementary line of research, we consider protecting the CSP from a dishonest owner, who attempts to get illegal compensations by falsely claiming data corruption over cloud servers. We propose a new cloud-based storage scheme that allows the data owner to benefit from the facilities offered by the CSP and enables mutual trust between them. In addition, the proposed scheme ensures that authorized users receive the latest version of the outsourced data, and enables the owner to grant or revoke access to the data stored by cloud servers.

Integrity constraints comprise an urgent part of conceptual models. For this reason, they must be structured and represented in orderly manner. The purpose of this work is to solve the problems inherent in creation, storage and retrieval of centralized data integrity constraints. This paper analyses the methods for implementation of data integrity constraints and describes a Oracle RDBMS-based technique that enables for impeccable functionality of information system and protection from invalid data. After analysis of system requirements and currently available software solutions, UML models of system architecture, data and database were proposed. Suitable means were chosen for developing a system for centralist management of data integrity constraints. The Constraint Editor geared towards designing, implementing and maintaining of procedures, integrity constraints throughout their lifecycle was developed. A simple, functional and intuitive graphical user interface makes data integrity constraints easy to story, manage and retrieve. The created software makes optimal use of RDBVS resources while performing ETL processes with multi – table insertions. The ability to insert data into multiple tables ensures further possibilities of supplying standard statistical analysis software with correct data and better performance than a procedure to perform multiple insertions using "IF..THEN" syntax. Furthermore, the software verifies SQL DML functions system widethus protecting... [to full text]

In the current era of digital world, the amount of sensitive data produced by many organizations is outpacing their storage ability. The management of such huge amount of data is quite expensive due to the requirements of high storage capacity and qualified personnel. Storage-as-a-Service (SaaS) offered by cloud service providers (CSPs) is a paid facility that enables organizations to outsource their data to be stored on remote servers. Thus, SaaS reduces the maintenance cost and mitigates the burden of large local data storage at the organization's end. For an increased level of scalability, availability and durability, some customers may want their data to be replicated on multiple servers across multiple data centers. The more copies the CSP is asked to store, the more fees the customers are charged. Therefore, customers need to have a strong guarantee that the CSP is storing all data copies that are agreed upon in the service contract, and these copies remain intact. In this thesis we address the problem of creating multiple copies of a data file and verifying those copies stored on untrusted cloud servers. We propose a pairing-based provable multi-copy data possession (PB-PMDP) scheme, which provides an evidence that all outsourced copies are actually stored and remain intact. Moreover, it allows authorized users (i.e., those who have the right to access the owner's file) to seamlessly access the file copies stored by the CSP, and supports public verifiability. We then direct our study to the dynamic behavior of outsourced data, where the data owner is capable of not only archiving and accessing the data copies stored by the CSP, but also updating and scaling (using block operations: modification, insertion, deletion, and append) these copies on the remote servers. We propose a new map-based provable multi-copy dynamic data possession (MB-PMDDP) scheme that verifies the intactness and consistency of outsourced dynamic multiple data copies. To the best of our knowledge, the proposed scheme is the first to verify the integrity of multiple copies of dynamic data over untrusted cloud servers. As a complementary line of research, we consider protecting the CSP from a dishonest owner, who attempts to get illegal compensations by falsely claiming data corruption over cloud servers. We propose a new cloud-based storage scheme that allows the data owner to benefit from the facilities offered by the CSP and enables mutual trust between them. In addition, the proposed scheme ensures that authorized users receive the latest version of the outsourced data, and enables the owner to grant or revoke access to the data stored by cloud servers.

With the advent of big data era, there is countless data produced from various kinds of machines every second. These data are used to help people to do studies, produce goods, improve the efficiency of the industrial development, and so on. Especially considering the Internet of things which connects all the smart machines together, the importance of data integrity gets unprecedented attention from us. In order to keep the data integrity, blockchain comes to its birth using its own structure to guarantee the data integrity efficiently. This project is focus on the simulation of data system based on the blockchain in the background of industrial internet of things which is shown in a form of a website with coding language jade and the environment is Node.Js. The main task is to measure the time consumed in the process of block mining under different parameters include data length, nonce, difficulty, sensor number in order to find the correlation between block mining time and different parameters. Thus, the rule about the impact of different parameter on the mining time are concluded. According to the rule I found, the evaluation about scalability, efficiency and safety of this system are given and I also summarize two formulas to calculate the efficiency of block mining. Ethical consideration and future work are addressed in the conclusion part.

It is a common problem in psychology subject pools for past study participants to inform future participants of key experimental details (also known as crosstalk). Previous research (Edlund, Sagarin, Skowronski, Johnson, & Kutter, 2009) demonstrated that a combined classroom and laboratory treatment could significantly reduce crosstalk. The present investigation tested a laboratory-only treatment for the prevention of crosstalk at five universities, along with institutional-level moderators of crosstalk. Results indicated the presence of crosstalk at all universities and that the laboratory-based treatment was effective in reducing crosstalk. Importantly, crosstalk rates were higher (but successfully neutralized) in research pools with higher research credit requirements. Therefore, this research provides valuable guidance regarding crosstalk prevalence and its minimization by researchers.

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With the growing popularity of e-commerce applications that use software agents, the protection of mobile agent data has become imperative. To that end, the performance of four methods that protect the data integrity of mobile agents is evaluated. The methods investigated include existing approaches known as the Partial Result Authentication Codes, Hash Chaining, and Set Authentication Code methods, and a technique of our own design, called the Modified Set Authentication Code method, which addresses the limitations of the Set Authentication Code method. The experiments were run using the DADS agent system (developed at the Network Research Laboratory at UNT), for which a Data Integrity Module was designed. The experimental results show that our Modified Set Authentication Code technique performed comparably to the Set Authentication Code method.