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Cyber Security Crimes, Ethics and a Suggested Algorithm to Overcome Cyber-Physical Systems Problems (CybSec1)

Journal of Information Security 2021年第1期12卷 56-78页

作者： abou_el_ela abdou hussien Computer Science Department Modern Academy-Maddi ARE Maddi Egypt

Digital systems have changed our world and will continue to change it. Supportive government policy, a strong research base and history of industrial success place the benefits of an emerging digital society. Protecting benefits and minimizing risks requires reliable and robust cyber security, backed by a robust research and translation system. Trust is essential for growth and maintenance of participation in the digital community. Organizations gain trust by acting in a trustworthy way leading to building reliable and secure systems, treating people, their privacy and their data with respect, and providing reliable and understandable information to help people understand how safe they are. Research and revolution in industry and academia will continue to make important contributions to create flexible and reliable digital environment. Cyber Security has a main role in the field of information technology because securing information has become one of the greatest challenges today. When we think about the cyber security, the first thing that comes to our mind is “cyber crimes” which are increasing exponentially day by day. Many governments and firms are taking many measures to prevent these cybercrimes. Besides the various measures, cyber security remains a major concern. This paper intended to give a

关键词： Cyber Security Cybercrime Cyber Ethics Social Media Cloud Computing Android Apps

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Attacks on Anonymization-Based Privacy-Preserving: A Survey for Data Mining and Data Publishing

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Journal of Information Security 2013年第2期4卷 101-112页

作者： abou-el-ela abdou hussien Nermin Hamza Hesham A. Hefny Department of Computer and Information Sciences Institute of Statistical Studies and Research Cairo University Giza Egypt Department of Computer Science Faculty of Science and Humanities Shaqra University Shaqra KSA

Data mining is the extraction of vast interesting patterns or knowledge from huge amount of data. The initial idea of privacy-preserving data mining PPDM was to extend traditional data mining techniques to work with the data modified to mask sensitive information. The key issues were how to modify the data and how to recover the data mining result from the modified data. Privacy-preserving data mining considers the problem of running data mining algorithms on confidential data that is not supposed to be revealed even to the party running the algorithm. In contrast, privacy-preserving data publishing (PPDP) may not necessarily be tied to a specific data mining task, and the data mining task may be unknown at the time of data publishing. PPDP studies how to transform raw data into a version that is immunized against privacy attacks but that still supports effective data mining tasks. Privacy-preserving for both data mining (PPDM) and data publishing (PPDP) has become increasingly popular because it allows sharing of privacy sensitive data for analysis purposes. One well studied approach is the k-anonymity model [1] which in turn led to other models such as confidence bounding, l-diversity, t-closeness, (α,k)-anonymity, etc. In particular, all known mechanisms try to minimize information loss and such an attempt provides a loophole for attacks. The aim of this paper is to present a survey for most of the common attacks techniques for anonymization-based PPDM & PPDP and explain their effects on Data Privacy.

关键词： Privacy k-Anonymity Data Mining Privacy-Preserving Data Publishing Privacy-Preserving Data Mining

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Utility-Based Anonymization Using Generalization Boundaries to Protect Sensitive Attributes

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Journal of Information Security 2015年第3期6卷 179-196页

作者： abou-el-ela abdou hussien Nagy Ramadan Darwish Hesham A. Hefny Department of Computer Science Faculty of Science and Arts Shaqra University Shaqra KSA Department of Computer and Information Sciences Institute of Statistical Studies and Research Cairo University Cairo Egypt

Privacy preserving data mining (PPDM) has become more and more important because it allows sharing of privacy sensitive data for analytical purposes. A big number of privacy techniques were developed most of which used the k-anonymity property which have many shortcomings, so other privacy techniques were introduced (l-diversity, p-sensitive k-anonymity, (α, k)-anonymity, t-closeness, etc.). While they are different in their methods and quality of their results, they all focus first on masking the data, and then protecting the quality of the data. This paper is concerned with providing an enhanced privacy technique that combines some anonymity techniques to maintain both privacy and data utility by considering the sensitivity values of attributes in queries using sensitivity weights which determine taking in account utility-based anonymization and then only queries having sensitive attributes whose values exceed threshold are to be changed using generalization boundaries. The threshold value is calculated depending on the different weights assigned to individual attributes which take into account the utility of each attribute and those particular attributes whose total weights exceed the threshold values is changed using generalization boundaries and the other queries can be directly published. Experiment results using UT dallas anonymization toolbox on real data set adult database from the UC machine learning repository show that although the proposed technique preserves privacy, it also can maintain the utility of the publishing data.

关键词： Privacy Privacy Preserving Data Mining K-Anonymity Generalization Boundaries Suppression

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