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New technologies and possibilities of simultaneous processing of large quantities of personal data are developing both in science and the economy. However, collecting and processing personal data opens up a whole range of issues and controversies. The European Union has also recognized the importance of this issue and has implemented a reform of personal data protection through a new instrument aimed at ensuring the protection of the rights and fundamental freedoms of individuals with regard to the processing of their personal data. It is the General Regulation (EU) 2016/679, which came into force in May 2016 and which will be applicable in the Member States from May 2018. Following this, a survey was conducted among students at an institution of higher education in Croatia (N=159), with the aim of examining their knowledge of the protection of individuals regarding the processing of personal data and the free movement of such data. The results show that the largest number of students are not capable of defining the term "personal data" to a sufficient degree, but can differentiate them from other data. Given that there are various study programs at an institution of higher education in Croatia, including programs in the field of information and communication technologies (IT), it is expected that the students of these study programs attach greater importance to the value of personal data and knowledge about them. [ABSTRACT FROM AUTHOR]

The number of phishing attacks against web serviceshas seen a steady increase causing, for example, a negative effecton the ability of banking and financial institutions to deliverreliable services on the Internet. This paper presents an automaticapproach detecting phishing attacks. Our approach combinesa personalized whitelisting approach with machine learningtechniques. The whitelist is used as filter that blocks phish webpages used to imitate innocuous user behavior. The phishingpages that are not blocked by the whitelist pass are furtherfiltered using a Support Vector Machine classifier designed andoptimized to classify these threats. Our experimental results showthat the proposed approach improves over the current state-ofthe-art methods. [ABSTRACT FROM PUBLISHER]

With the rapid development of Internet, online information has greatly enriched. The Internet becomes a vast treasure of information, but simultaneously it is also flooding various trash information, such as: viruses, Trojans, violence, pornography, gambling and so on. The hostile forces outside of country and criminal elements are using the Internet to engage in illegal activities that endanger national security. So how to recognize this information to find the corresponding website and to carry on the effective supervision has become an urgent problem. For these reasons, this paper designs a new web information extraction system, which calls the extraction rule corresponding to the template by calculating the structural similarity among pages. In addition, a new method based on STU-DOM tree to construct decision tree is proposed. This method can use the classification of decision tree to determine sensitive information node. [ABSTRACT FROM PUBLISHER]