| | SLO | ENG | Cookies and privacy

Bigger font | Smaller font

Search the digital library catalog Help

Query: search in
search in
search in
search in
* old and bologna study programme

Options:
  Reset


1 - 6 / 6
First pagePrevious page1Next pageLast page
1.
An algorithm for protecting knowledge discovery data
Boštjan Brumen, Izidor Golob, Tatjana Welzer Družovec, Ivan Rozman, Marjan Družovec, Hannu Jaakkola, 2003, original scientific article

Abstract: In the paper, we present an algorithm that can be applied to protect data before a data mining process takes place. The data mining, a part of the knowledge discovery process, is mainly about building models from data. We address the following question: can we protect the data and still allow the data modelling process to take place? We consider the case where the distributions of original data values are preserved while the values themselves change, so that the resulting model is equivalent to the one built with original data. The presented formal approach is especially useful when the knowledge discovery process is outsourced. The application of the algorithm is demonstrated through an example.
Keywords: data protection algorithm, classification algorithm, disclosure control, data mining, knowledge discovery, data security
Published: 01.06.2012; Views: 1363; Downloads: 29
URL Link to full text

2.
3.
4.
5.
Outsourcing medical data analyses
Boštjan Brumen, Marjan Heričko, Andrej Sevčnikar, Jernej Završnik, Marko Hölbl, 2013, original scientific article

Abstract: Background: Medical data are gold mines for deriving the knowledge that could change the course of a single patient’s life or even the health of the entire population. A data analyst needs to have full access to relevant data, but full access may be denied by privacy and confidentiality of medical data legal regulations, especially when the data analyst is not affiliated with the data owner. Objective: Our first objective was to analyze the privacy and confidentiality issues and the associated regulations pertaining to medical data, and to identify technologies to properly address these issues. Our second objective was to develop a procedure to protect medical data in such a way that the outsourced analyst would be capable of doing analyses on protected data and the results would be comparable, if not the same, as if they had been done on the original data. Specifically, our hypothesis was there would not be a difference between the outsourced decision trees built on encrypted data and the ones built on original data. Methods: Using formal definitions, we developed an algorithm to protect medical data for outsourced analyses. The algorithm was applied to publicly available datasets (N=30) from the medical and life sciences fields. The analyses were performed on the original and the protected datasets and the results of the analyses were compared. Bootstrapped paired t tests for 2 dependent samples were used to test whether the mean differences in size, number of leaves, and the accuracy of the original and the encrypted decision trees were significantly different. Results: The decision trees built on encrypted data were virtually the same as those built on original data. Out of 30 datasets, 100% of the trees had identical accuracy. The size of a tree and the number of leaves was different only once (1/30, 3%, P=.19). Conclusions: The proposed algorithm encrypts a file with plain text medical data into an encrypted file with the data protected in such a way that external data analyses are still possible. The results show that the results of analyses on original and on protected data are identical or comparably similar. The approach addresses the privacy and confidentiality issues that arise with medical data and is adherent to strict legal rules in the United States and Europe regarding the processing of the medical data.
Keywords: medical data, disclosure control, medical confidentiality, data analysis, data security
Published: 02.08.2017; Views: 488; Downloads: 59
.pdf Full text (3,34 MB)
This document has many files! More...

6.
Security analysis and improvements to the psychopass method
Boštjan Brumen, Marjan Heričko, Ivan Rozman, Marko Hölbl, 2013, original scientific article

Abstract: Background: In a recent paper, Pietro Cipresso et al proposed the PsychoPass method, a simple way to create strong passwords that are easy to remember. However, the method has some security issues that need to be addressed. Objective: To perform a security analysis on the PsychoPass method and outline the limitations of and possible improvements to the method. Methods: We used the brute force analysis and dictionary attack analysis of the PsychoPass method to outline its weaknesses. Results: The first issue with the Psychopass method is that it requires the password reproduction on the same keyboard layout as was used to generate the password. The second issue is a security weakness: although the produced password is 24 characters long, the password is still weak. We elaborate on the weakness and propose a solution that produces strong passwords. The proposed version first requires the use of the SHIFT and ALT-GR keys in combination with other keys, and second, the keys need to be 1-2 distances apart. Conclusions: The proposed improved PsychoPass method yields passwords that can be broken only in hundreds of years based on current computing powers. The proposed PsychoPass method requires 10 keys, as opposed to 20 keys in the original method, for comparable password strength.
Keywords: passwords, cryptanalysis, data security
Published: 02.08.2017; Views: 411; Downloads: 319
.pdf Full text (542,01 KB)
This document has many files! More...

Search done in 0.17 sec.
Back to top
Logos of partners University of Maribor University of Ljubljana University of Primorska University of Nova Gorica