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On the Role of Data Anonymization in Machine Learning Privacy
International Conference on Trust, Security and Privacy in Computing and Communications, 2020Data anonymization irrecoverably transforms the raw data into a protected version by eliminating direct identifiers and removing sufficient details from indirect identifiers in order to minimize the risk of re-identification when there is a requirement ...
Navoda Senavirathne, V. Torra
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Implications of Data Anonymization on the Statistical Evidence of Disparity
Management Sciences, 2020Research and practical development of data-anonymization techniques have proliferated in recent years. Yet, limited attention has been paid to examine the potentially disparate impact of privacy protection on underprivileged subpopulations. This study is
Heng Xu, N. Zhang
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Application of data anonymization in Learning Analytics
Proceedings of the 3rd International Conference on Applications of Intelligent Systems, 2020Thanks to the proliferation of academic services on the Web and the opening of educational content, today, students can access a large number of free learning resources, and interact with value-added services.
Janneth Chicaiza +3 more
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Checking Anonymity Levels for Anonymized Data
2011Privacy Preserving Publication has become one of the most prominent research topics in the recent years. Several techniques like kanonymity, l-diversity and (α, k) anonymity were proposed to preserve privacy. Most of the published work focuses on anonymizing the microdata for preserving privacy and now the focus towards the verification of the ...
V. Valli Kumari +4 more
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A Comparative Study of Data Anonymization Techniques
2019 IEEE 5th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS), 2019In today's digital era, it is a very common practice for organizations to collect data from individual users. The collected data is then stored in multiple databases which contain personally identifiable information (PII). This may lead to a major source
S. Murthy +3 more
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Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, 2009
Sharing healthcare data has become a vital requirement in healthcare system management; however, inappropriate sharing and usage of healthcare data could threaten patients' privacy. In this paper, we study the privacy concerns of the blood transfusion information-sharing system between the Hong Kong Red Cross Blood Transfusion Service (BTS) and public ...
Noman Mohammed +3 more
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Sharing healthcare data has become a vital requirement in healthcare system management; however, inappropriate sharing and usage of healthcare data could threaten patients' privacy. In this paper, we study the privacy concerns of the blood transfusion information-sharing system between the Hong Kong Red Cross Blood Transfusion Service (BTS) and public ...
Noman Mohammed +3 more
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Data Anonymization for Privacy Protection in Fog-Enhanced Smart Homes
International Computer Science Conference, 2020Smart homes are revolutionizing lives of their residents in multiple ways. Benefits range from bringing convenience in day to day life, optimization of decision processes to use of their data for analytics by businesses.
Vartika Puri +2 more
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Data anonymization: k-anonymity and de-anonymization attacks
2017Η ανάγκη για πρόσβαση σε δεδομένα που αφορούν πολίτες ολοένα αυξάνεται τα τελευταία χρόνια. Αυτά τα δεδομένα μπορεί να έχουν συλλεχθεί από κυβερνήσεις ή επιχειρήσεις για διάφορους σκοπούς και λόγους. Παρόλα αυτά η δημοσίευση των δεδομένων μπορεί να προκαλέσει διάφορα θέματα εάν δεν ληφθούν κατάλληλα μέτρα.
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Collaborative Data Anonymization for Privacy-Preserving Vehicular Ad-hoc Network
2020 International Conference on Innovation and Intelligence for Informatics, Computing and Technologies (3ICT), 2020Optimization of data usability and privacy protection is a challenging goal. Although the released data are sanitized with the upgraded model of data semantic changer, always face privacy and scalability issues. On the other hand, local anonymization may
Tarak Nandy +4 more
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Big data anonymization with spark
2017 International Conference on Computer Science and Engineering (UBMK), 2017Privacy is an important issue for big data including sensitive attributes. In the case of directly sharing or publishing these data, privacy breach occurs. In order to overcome this problem, previous studies were focused on developing big data anonymization techniques on Hadoop environment.
SAĞIROĞLU, ŞEREF, Canbay, Yavuz
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