This master’s thesis focuses on implementation of ABC (Anonymous attribute-based credential) pilot system on the Android platform. The support for attribute authentication on the Android platform is very weak in terms of the number of implementations and needs a special attention. The theoretical part of the thesis describes the cryptographic support on the Android platform, the use of the Android Native Development Kit (NDK) and the Host-Card Emulation (HCE) service. The theoretical part of the thesis also includes a description of attribute authentication schemes, including a pilot RKVAC system. The practical part describes the implementation of the RKVAC system on the Android platform along with the implementation of a custom cryptographic kernel based on the native MCL cryptographic library. The practical part of this thesis describes implementation proces of RKVAC system on Android plaform, that uses native cryptographic library MCL. The final part shows the results of time, memory and computation difficulty of developed applications.

abstract: The traditional access control system suffers from the problem of separation of data ownership and management. It poses data security issues in application scenarios such as cloud computing and blockchain where the data owners either do not trust the data storage provider or even do not know who would have access to their data once they are appended to the chain. In these scenarios, the data owner actually loses control of the data once they are uploaded to the outside storage. Encryption-before-uploading is the way to solve this issue, however traditional encryption schemes such as AES, RSA, ECC, bring about great overheads in key management on the data owner end and could not provide fine-grained access control as well. Attribute-Based Encryption (ABE) is a cryptographic way to implement attribute-based access control, which is a fine-grained access control model, thus solving all aforementioned issues. With ABE, the data owner would encrypt the data by a self-defined access control policy before uploading the data. The access control policy is an AND-OR boolean formula over attributes. Only users with attributes that satisfy the access control policy could decrypt the ciphertext. However the existing ABE schemes do not provide some important features in practical applications, e.g., user revocation and attribute expiration. Furthermore, most existing work focus on how to use ABE to protect cloud stored data, while not the blockchain applications. The main objective of this thesis is to provide solutions to add two important features of the ABE schemes, i.e., user revocation and attribute expiration, and also provide a practical trust framework for using ABE to protect blockchain data. To add the feature of user revocation, I propose to add user's hierarchical identity into the private attribute key. In this way, only users whose identity is not revoked and attributes satisfy the access control policy could decrypt the ciphertext. To add the feature of attribute expiration, I propose to add the attribute valid time period into the private attribute key. The data would be encrypted by access control policy where all attributes have a temporal value. In this way, only users whose attributes both satisfy the access policy and at the same time these attributes do not expire, are allowed to decrypt the ciphertext. To use ABE in the blockchain applications, I propose an ABE-enabled trust framework in a very popular blockchain platform, Hyperledger Fabric. Based on the design, I implement a light-weight attribute certificate authority for attribute distribution and validation; I implement the proposed ABE schemes and provide a toolkit which supports system setup, key generation, data encryption and data decryption. All these modules were integrated into a demo system for protecting sensitive les in a blockchain application. / Dissertation/Thesis / Masters Thesis Computer Science 2020

System complexity has continued to increase with the development and application of new technologies. This increased complexity has created great concerns among people about the potential impact of a system on its ecological environment when considering such as plants, wildlife and clean air. A complete awareness of the potential impact requires a thorough understanding of how a system interacts with its ecological environment, and the results are dependent on the expertise of the engineer who is responsible for the design of the system and the analyst who evaluates the system Due to the complexity of these interactions and the difficulty in measuring the appropriate cause-and-effect relationships, a system's impact on its ecological environment has not received due attention. The above complexity and difficulty have led to two deficiencies in the current research of the system's environmental impact. One is the insufficient evaluation of its qualitative attributes. The other is an unstructured evaluation process where the analyst has to rely on qualitative attributes as major inputs while his/her expertise could not be modeled. As a consequence, the current research and evaluation process is deficient because of biases and lack of clarity. This report seeks to instill the necessary clarity into the decision-making process by structuring the decision maker's subjective knowledge. It is concluded that subjective preferences can be quantified and evaluated through utility function assessment. Alternatives are ranked and a final choice is made based on their utility. The modeling process described herein is made a lot more efficient and economical because of the computer software that integrates the assessment mechanisms into a user-friendly operational environment. After the deficiencies in the current evaluation process are identified, possible solutions are explored. The effectiveness of the Analytic Hierarchy Process (AHP), Multi-attribute Value Theory (MA VT), and Multi-attribute Utility Theory (MAUT) are compared. MAUT is the preferred approach based on solution requirements. / Master of Science

This thesis focuses on the algorithm selection problem, in which the goal is to recommend machine learning algorithms to a new dataset. The idea behind solving this issue is that algorithm performs similarly on similar datasets. The usual approach is to base the similarity measure on the fixed vector of metafeatures extracted out of each dataset. However, as the number of attributes among datasets varies, we may be loosing important information. Herein, we propose a family of algorithms able to handle even the non-propositional representations of datasets. Our methods use the idea of attribute assignment that builds the distance measure between datasets as a sum of distance given by the optimal assignment and an attribute distance measure. Furthermore, we prove that under certain conditions, we can guarantee the resulting dataset distance to be a metric. We carry out a series of metalearning experiments on the data extracted from the OpenML repository. We build up attribute distance using Genetic Algorithms, Genetic Programming and several regularization techniques such as multi-objectivization, coevolution, and bootstrapping. The experiment indicates that the resulting dataset distance can be successfully applied on the algorithm selection problem. Although we use the proposed distance measures exclusively...

No description available.

When classifying high-dimensional data, a lot can be gained, in terms of both computational time and precision, by only considering the most important features. Many feature selection methods are based on the assumption that important features are highly correlated with their corresponding classes, but mainly uncorrelated with each other. Often, this assumption can help eliminate redundancies and produce good predictors using only a small subset of features. However, when the predictability depends on interactions between the features, such methods will fail to produce satisfactory results. Also, since the suitability of the selected features depends on the learning algorithm in which they will be used, correlation-based filter methods might not be optimal when using genetic programs as the final classifiers, as they fail to capture the possibly complex relationships that are expressible by the genetic programming rules. In this thesis a method that can find important features, both independently and dependently discriminative, is introduced. This method works by performing two different types of permutation tests that classifies each of the features as either irrelevant, independently predictive or dependently predictive. The proposed method directly evaluates the suitability of the features with respect to the learning algorithm in question. Also, in contrast to computationally expensive wrapper methods that require several subsets of features to be evaluated, a feature classification can be obtained after only one single pass, even though the time required does equal the training time of the classifier. The evaluation shows that the attributes chosen by the permutation tests always yield a classifier at least as good as the one obtained when all attributes are used during training - and often better. The proposed method also fares well when compared to other attribute selection methods such as RELIEFF and CFS. / Då man handskas med data av hög dimensionalitet kan man uppnå både bättre precision och förkortad exekveringstid genom att enbart fokusera på de viktigaste attributen. Många metoder för att hitta viktiga attribut är baserade på ett grundantagande om en stark korrelation mellan de viktiga attributen och dess tillhörande klass, men ofta även på ett oberoende mellan de individuella attributen. Detta kan å ena sidan leda till att överflödiga attribut lätt kan elimineras och därmed underlätta processen att hitta en bra klassifierare, men å andra sidan också ge missvisande resultat ifall förmågan att separera klasser i hög grad beror på interaktioner mellan olika attribut. Då lämpligheten av de valda attributen också beror på inlärningsalgoritmen i fråga är det troligtvis inte optimalt att använda sig av metoder som är baserade på korrelationer mellan individuella attribut och dess tillhörande klass, ifall målet är att skapa klassifierare i form av genetiska program, då sådana metoder troligtvis inte har förmågan att fånga de komplexa interaktioner som genetiska program faktiskt möjliggör. Det här arbetet introducerar en metod för att hitta viktiga attribut - både de som kan klassifiera data relativt oberoende och de som får sina krafter endast genom att utnyttja beroenden av andra attribut. Den föreslagna metoden baserar sig på två olika typer av permutationstester, där attribut permuteras mellan de olika dataexemplaren för att sedan klassifieras som antingen oberende, beroende eller irrelevanta. Lämpligheten av ett attribut utvärderas direkt med hänsyn till den valda inlärningsalgoritmen till skillnad från så kallade wrappers, som är tidskrävande då de kräver att flera delmängder av attribut utvärderas. Resultaten visar att de attribut som ansetts viktiga efter permutationstesten genererar klassifierare som är åtminstone lika bra som när alla attribut används, men ofta bättre. Metoden står sig också bra när den jämförs med andra metoder som till exempel RELIEFF och CFS.

In this paper, we discuss the deficiencies of normal form definitions based on Functional Dependency and introduce a new normal form concept based on Functional Independency. Functional Independency has not been systematically investigated while there is a very strong theoretical foundation for the study of Functional Dependency in relational database normalization. This paper will demonstrate that considering Functional Dependency alone cannot eliminate some common data anomalies and the normalization process can yield better database designs with the addition of Functional Independency.

Background: Incorporating microtransactions has become a popular business model for video game publishers as it ultimately provides stability and reduces financial risk. While microtransactions are becoming increasingly prevalent, they do not come without trouble as they have been linked to unfair gameplay advantages and reduced satisfaction with the game. However, understanding and meeting players’ perceived values mean that developers can incorporate microtransactions more effectively and avoid such concerns. Problem: Despite comprehensive research and the consistent recognition of perceived values as having a significant impact on the propensity to purchase microtransactions, the existing literature has yielded inconclusive results regarding the precise extent of their influence. Furthermore, the current body of literature has mainly focused on multiplayer games which differ greatly in their nature compared with single-player games, meaning that these findings do not necessarily apply in a single-player environment. Thus, very little is known about what happens when the social element is absent in the game and more research is needed on single-player games to understand this impact, where perceptions can be explored in another setting yielding potentially different results. Purpose: The research seeks to examine the influence of social elements on players’ attitudes toward purchasing microtransactions and build a theoretical framework. With the absence of research on single-player games and the distinction to multiplayer modes, emergent findings will elaborate adjacent theories on microtransactional gaming and provide an increasingly nuanced understanding of the topic. Research Question: How does the social element of gaming impact players’ willingness to purchase microtransactions? Method: Ontology – Interpretivism; Epistemology – Social Constructionism; Methodology – Inductive Exploratory Multiple Case Study; Data Collection – 10 Semi-structured Interviews; Interview Technique – Laddering Technique; Sampling –Purposeful Sampling and Snowball Sampling; Data Analysis – Thematic Content Analysis. Conclusion: The present study provides evidence of four distinct perceived values, namely self-expression, emotional, convenience, and social, which exert influence on players' willingness to purchase microtransactions. Notably, the social aspects within the gaming environment were found to differentially impact each of these values. Furthermore, these perceived values were found to possess a temporal dimension, indicating whether the anticipated benefits would be realized immediately, in the future, or both. To determine the overall perceived value and purchase intent, players assess the expected benefits of perceived immediate and future value relative to the purchase's associated costs and perceived social risks.

The digitalization of society is a major trend that is currently transforming the businesslandscape (Parviainen et al., 2017, p. 63). Within this digital era, the AI chatbot hasemerged (Hsu & Lin, 2023, p. 2), enabling companies to provide high-quality customerservice 24/7. AI chatbots offer a range of benefits and possibilities that can be utilized invarious industries. There is evidence that AI chatbots have the potential to create loyalcustomers, making it a valuable tool for businesses. Therefore, this thesis investigateswhat AI chatbot attributes can relate to the two dimensions of customer loyalty amongGen Z.The purpose of this research is to help organizations to build relationships with customersand provide insight into which attributes of an AI chatbot can be used by companies tocreate value for customers. This to further facilitate their work of building customerloyalty. In this study, we utilize a framework to quantitatively measure AI chatbotsthrough various attributes, including Availability, Ease of Use, Accuracy of Response,Responsiveness, Assurance, and Empathy. Then the relationship of the attributes washypothesized together with the two dimensions of customer loyalty, attitudinal andbehavior loyalty. The questionnaires was distributed to Gen Z through snowball and self-selection sampling techniques. Through the online survey, 73 responses were collected totest the hypothesis. It was found that empathy has a significant relationship with bothdimensions of customer loyalty, and assurance has a significant relationship withattitudinal loyalty.We are convinced that this strengthens and reinforces the ongoing research on customerloyalty. Moreover, the study expands the quantitative work on AI chatbots by introducingand utilizing a framework to examine the attributes of AI chatbots in a general context.Furthermore, the thesis provides knowledge to companies that are currently working onor developing AI chatbots, of which attributes that should be prioritized to enhancecustomer loyalty. Our findings suggest that these attributes are assurance and empathy.

No description available.