Achieving k-Anonymity Privacy Protection Using Generalization and Suppression
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Summary
This paper provides a formal presentation of combining generalization and suppression to achieve k-anonymity and shows that Datafly can over distort data and µ-Argus can additionally fail to provide adequate protection.
- Type
- article
- Published
- 2002-10-01
- Cited by
- 2,152
- References
- 12
- OpenAlex
- https://openalex.org/W2119047901
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:1424892
Keywords
k-anonymity, Anonymity, Generalization, Heuristics, Computer science
References
- Confidentiality, Disclosure and Data Access: Theory and Practical Applications for Statistical Agencies
- Computational disclosure control: a primer on data privacy protection
- Guaranteeing anonymity when sharing medical data, the Datafly System
- k-Anonymity: A Model for Protecting Privacy
- Principles of database and knowledge- base systems
- Experimental Gerontology: Acknowledgments
- Principles of Database and Knowledge-Base Systems, Volume II
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- Time-varying phase relationship between spiking neuronal responses and local field potentials in olfactory processing in manduca sexta
- Protecting privacy using k-anonymity.
- Association Analysis of Semi-structured Data for Discrimination Discovery in Business
- Enforcing privacy via access control and data perturbation.
- Bootstrapping location-aware personal computing
- An Enhanced Utility-Driven Data Anonymization Method
- Privacy Preserving Distributed Data Mining
- An Efficient Big Data Anonymization Algorithm Based on Chaos and Perturbation Techniques
- Survey of k-Anonymity
- Location Privacy in Location-Based Services: Beyond TTP-based Schemes
- Privacy Preserving Data Publication of Dynamic Datasets
- Publishing Skewed Sensitive Microdata
- Inter-temporal Privacy Metrics
- Privacy elicitation and utilization in distributed data exchange systems
- On the utility of randomization approaches for privacy preserving data publishing
- De-identified multidimensional medical records for disease population demographics and image processing tools development
- How friendship links and group memberships affect the privacy of individuals in social networks
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