Protecting Respondents' Identities in Microdata Release
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Summary
This paper addresses the problem of releasing microdata while safeguarding the anonymity of respondents to which the data refer and introduces the concept of minimal generalization that captures the property of the release process not distorting the data more than needed to achieve k-anonymity.
- Type
- article
- Published
- 2001-11-01
- Cited by
- 2,524
- References
- 27
- OpenAlex
- https://openalex.org/W2119067110
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:561716
Keywords
Microdata (statistics), Computer science, k-anonymity, Anonymity, Identifier
References
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- Information Security: An Integrated Collection of Essays
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- Security-control methods for statistical databases: a comparative study
- Information privacy issues for the 1990s
- Detection and elimination of inference channels in multilevel relational database systems
- Aggregation and inference: facts and fallacies
- A security policy model for clinical information systems
- IEEE Transactions on Knowledge and Data Engineering
- Private Lives and Public Policies
- Suppression Methodology and Statistical Disclosure Control
- Introduction to lattices and order
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- Balancing between data utility and privacy preservation in data mining
- Protecting privacy using k-anonymity.
- Anonymization of Statistical Data
- Enforcing privacy via access control and data perturbation.
- Small sum privacy and large sum utility in data publishing
- Evaluating the Privacy Implications of Frequent Itemset Disclosure
- An Enhanced Utility-Driven Data Anonymization Method
- A Multi-Path Approach for k-Anonymity in Mobile Hybrid Networks
- Data Leak Detection as a Service
- k-Anonymity in Context of Digitally Signed CDA Documents
- An Efficient Big Data Anonymization Algorithm Based on Chaos and Perturbation Techniques
- P-Sensitive K-Anonymity for Social Networks
- On Anonymization of String Data
- Location Privacy in Location-Based Services: Beyond TTP-based Schemes
- Privacy Preserving Data Publication of Dynamic Datasets
- A Survey on Privacy Preserving Data Mining Techniques
- Ambiguity: Hide the Presence of Individuals and Their Privacy with Low Information Loss
- Publishing Skewed Sensitive Microdata
- Comparative study of distance metrics for t-closeness
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