Searching for "Privacy-Preserving Data Publishing." – sorted by Relevance.
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α, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing
- (α, k)-Anonymity: An Enhanced k-Anonymity Model for Privacy-Preserving Data Publishing Raymond Chi
- Cited by 14 (2 self) – Add To MetaCart
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Flexible anonymization for privacy preserving data publishing: A systematic search based approach
- Flexible Anonymization For Privacy Preserving Data Publishing: A Systematic Search Based Approach
- Cited by 2 (0 self) – Add To MetaCart
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FF-Anonymity: When Quasi-Identifiers Are Missing
- , and outline an efficient solution. I. INTRODUCTION Privacy-preserving data publishing (PPDP) focuses
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On the Anonymization of Sparse High-Dimensional Data
- — Existing research on privacy-preserving data publishing focuses on relational data: in this context
- Cited by 4 (1 self) – Add To MetaCart
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Ambiguity: Hide the Presence of Individuals and Their Privacy with Low Information Loss
- . However, the existing privacy-preserving data publishing techniques that can protect both presence privacy
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Preserving Privacy in Social Networks Against Neighborhood Attacks
- and more social network data has been published in one way or another, preserving privacy in publishing
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Publishing Sensitive Transactions for Itemset Utility
- there has been a great deal of works on privacy-preserving data publishing (PPDP) for relational data, led
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General Terms
- into the problem of privacy preserving data publishing where a data custodian needs to distribute an anonymized
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A Sampling-Based Approach to Information Recovery †
- the received data. • Information recovery for privacy-preserving data publishing (IR-PPDP). In privacy-preserving
- Cited by 1 (1 self) – Add To MetaCart
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A Monte Carlo Sampling Framework for Information Recovery ∗
- the received data. • Information recovery for privacy-preserving data publishing (IR- PPDP). In privacy-preserving
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