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Full-Text Articles in Databases and Information Systems
Privacy-Preserving Sanitization In Data Sharing, Wentian Lu
Privacy-Preserving Sanitization In Data Sharing, Wentian Lu
Doctoral Dissertations
In the era of big data, the prospect of analyzing, monitoring and investigating all sources of data starts to stand out in every aspect of our life. The benefit of such practices becomes concrete only when analysts or investigators have the information shared from data owners. However, privacy is one of the main barriers that disrupt the sharing behavior, due to the fear of disclosing sensitive information. This dissertation describes data sanitization methods that disguise the sensitive information before sharing a dataset and our criteria are always protecting privacy while preserving utility as much as possible. In particular, we provide …
Predicting Human Behavior, Tamara Kneese
Predicting Human Behavior, Tamara Kneese
Media Studies
Countless highly accurate predictions can be made from trace data, with varying degrees of personal or societal consequence (e.g., search engines predict hospital admission, gaming companies can predict compulsive gambling problems, government agencies predict criminal activity). Predicting human behavior can be both hugely beneficial and deeply problematic depending on the context. What kinds of predictive privacy harms are emerging? And what are the implications for systems of oversight and due process protections? For example, what are the implications for employment, health care and policing when predictive models are involved? How should varied organizations address what they can predict?
Network Security: Privacy-Preserving Data Publication: A Review On “Updates” In Continuous Data Publication, Adeel Anjum, Guillaume Raschia
Network Security: Privacy-Preserving Data Publication: A Review On “Updates” In Continuous Data Publication, Adeel Anjum, Guillaume Raschia
International Conference on Information and Communication Technologies
Preserving the privacy of individuals while publishing their relevant data has been an important problem. Most of previous works in privacy preserving data publication focus on one time, static release of datasets. In multiple publications however, where data is published multiple times, these techniques are unable to ensure privacy of the concerned individuals as just joining either of the releases could result in identity disclosure. In this work, we tried to investigate the major findings in the scenario of continuous data publication, in which the data is not only published multiple times but also modified with INSERTS, UPDATES and DELETE …
K-Anonymity In The Presence Of External Databases, Dimitris Sacharidis, Kyriakos Mouratidis, Dimitris Papadias
K-Anonymity In The Presence Of External Databases, Dimitris Sacharidis, Kyriakos Mouratidis, Dimitris Papadias
Kyriakos MOURATIDIS
The concept of k-anonymity has received considerable attention due to the need of several organizations to release microdata without revealing the identity of individuals. Although all previous k-anonymity techniques assume the existence of a public database (PD) that can be used to breach privacy, none utilizes PD during the anonymization process. Specifically, existing generalization algorithms create anonymous tables using only the microdata table (MT) to be published, independently of the external knowledge available. This omission leads to high information loss. Motivated by this observation we first introduce the concept of k-join-anonymity (KJA), which permits more effective generalization to reduce the …
K-Anonymity In The Presence Of External Databases, Dimitris Sacharidis, Kyriakos Mouratidis, Dimitris Papadias
K-Anonymity In The Presence Of External Databases, Dimitris Sacharidis, Kyriakos Mouratidis, Dimitris Papadias
Research Collection School Of Computing and Information Systems
The concept of k-anonymity has received considerable attention due to the need of several organizations to release microdata without revealing the identity of individuals. Although all previous k-anonymity techniques assume the existence of a public database (PD) that can be used to breach privacy, none utilizes PD during the anonymization process. Specifically, existing generalization algorithms create anonymous tables using only the microdata table (MT) to be published, independently of the external knowledge available. This omission leads to high information loss. Motivated by this observation we first introduce the concept of k-join-anonymity (KJA), which permits more effective generalization to reduce the …