In the paper we analyze the privacy and confidentiality issues and the associated regulations pertaining the medical data, and to identify technologies to properly address the issues. Secondly, the objective is to develop a procedure to protect medical data in such a way that the outsourced analyst is capable of doing the analyses on protected data, and the results are comparable, if not the same, as if they had been done on the original data. Using formal definitions, we developed an algorithm to protect medical data for outsourced analyses. The proposed algorithm encrypts a file with plaintext medical data into an encrypted file, where the data are protected in such a way that the external data analyses are still possible. The results show that in most cases the results of analyses on original and on protected data are identical or comparably similar.
COBISS.SI-ID: 16928790
Decision tree (DT) is one of the most popular symbolic machine learning approaches to classification with a wide range of applications. Decision treesare especially attractive in data mining. It has an intuitive representation and is, therefore, easy to understand and interpret, also by nontechnical experts. The most important and critical aspect of DTs is the process of their construction. Several induction algorithms exist that use therecursive top-down principle to divide training objects into subgroups based on different statistical measures in order to achieve homogeneous subgroups. Although being robust and fast, generally providing good results, their deterministic and heuristic nature can lead to suboptimal solutions. Therefore, alternative approaches have developed which try to overcome the drawbacks of classical induction. One of the most viable approaches seems to be the use of evolutionary algorithms, which can produce better DTs as they are searching for globally optimal solutions, evaluating potential solutions with regard to different criteria. We review the process of evolutionary design of DTs, providing the description of the most common approaches as wellas referring to recognized specializations. The overall process is first explained and later demonstrated in a step-by-step case study using a dataset from the University of California, Irvine (UCI) machine learning repository.
COBISS.SI-ID: 16733462
Two-party authenticated key agreement protocols using pairings have gained much attention in the cryptographic community. Several protocols of this type where proposed in the past of which many were found to be flawed. This resulted in attacks or the inability to conform to security attributes. In this paper, we propose an efficient identity-based authenticated key agreementprotocol employing pairings which employs a variant of a signature scheme and conforms to security attributes. Additionally, existing competitiveand the proposed protocol are compared regarding efficiency and security. The criteria for efficiency are defined in this paper, whereas the criteria for security are defined by the fulfilment of security attributes from literature.
COBISS.SI-ID: 14779926
Successful modeling tools need to effectively support individual as well as team-based work (collaboration) within colocated and virtual environments. In the past, achieving this has been challenging, since traditional modeling tools are desktop-based and thus suitable for individual and colocated work only. However, with the rise of web-based architectures and the cloud paradigm, desktop modeling tools now have rivals in their web-based counterparts that are especially suited for online collaboration (e-collaboration). The objective of our research was to probe the question as to 'which type of modeling tools (desktop or cloud-based) performs better in cases of individual work and e-collaboration', and to obtain insights into thesources of the strengths and weaknesses regarding both types of modeling tools. A controlled experiment was performed in which we addressed two types of modeling tools-desktop and cloud-based, in respect to two types of work-individual and e-collaboration. Within these treatments, we observed the productivity of 129 undergraduate IT students, who performed different types of modeling activities. The experimental participants reported no statistical significant differences between self-reported expertise with the investigated tools as well as their overall characteristics. However, they did finish individual and e-collaborative activities faster when using cloud modeling tool, where significant differences in favor of the cloud modeling tool were detected during e-collaboration. If we aggregate the results, we can argue that cloud modeling tools are comparable with desktop modeling tools during individual activities and outperform them during e-collaboration. Our findings also correlate with the related research, stating that with the use of state-of-the-art Web technologies, cloud-based applications can achieve the user experience of desktop applications.
COBISS.SI-ID: 17252886
Existing literature in the field of e-learning technology acceptance reflects a significant number of independent studies that primarily investigate the causal relationships proposed by technology acceptance theory, such as the technology acceptance model (TAM). To synthesize the existing knowledge in the field of e-learning technology acceptance, we have conducted a systematic literature review of 42 independent papers, mostly published in major journals. Furthermore, in order to view the research context by combining and analyzing the quantitative results of the reviewed research studies, a meta-analysis of the causal effect sizes between common TAM-related relationships was conducted. The main findings of this study, which is the first of its kind, are: (1) TAM is the most-used acceptance theory in e-learning acceptance research, and (2) the size of the causal effects between individual TAM-related factors depends on the type of user and the type of e-learning technology. The results of the meta-analysis demonstrated a moderating effect for user-related factors and technology-related factors for several evaluated causal paths. We have gathered proof that the perceived ease of use and the perceived usefulness tend to be the factors that can influence the attitudes of users toward using an e-learning technology in equal measure for different user types and types of e-learning technology settings.
COBISS.SI-ID: 15270166