k-Anonymity
By Valentina Ciriani, Sabrina De Capitani di Vimercati, Sara Foresti, Pierangela Samarati. 2007
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Nicolas Papernot
- Nicholas Carlini
- Philip S. Yu
- Charu C. Aggarwal
- Florian Tramèr
- Peter Kairouz
- Ú. Erlingsson
- Vitaly Shmatikov
Citations in the corpus
- Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data (2016, background): “For instance, k-anonymity requires information about an individual to be indistinguishable from at least k − 1 other individuals in the dataset (L. Sweeney, 2002).”
- How To Break Anonymity of the Netflix Prize Dataset (2006, background): “A popular approach to micro-data privacy is k -anonymity [24, 23, 8].”
- Data and Structural k-Anonymity in Social Networks (2009, methodology): “To protect the social network data, we mask it according to the k-anonymity model (every node will be indistinguishable with at least other (k-1) nodes) [6,22,24], in terms of both nodes’ attributes and nodes’ associated structural information (neighborhood).”
- Electronic health records: new opportunities for clinical research (2013, methodology): “These approaches usually encompass a combination of techniques such as generalization [56, 57], suppression [58], global recoding [59], Post RAndomi-sation Method (PRAM) [60], microaggregation [61], top and bottom coding [59] and slicing [62, 63].”
- Robust De-anonymization of Large Sparse Datasets (2008, methodology): “A popular approach to micro-data privacy is k -anonymity [27, 9].”
- Protecting privacy using k-anonymity. (2008, background): “42–45 Any record in a k-anonymized data set has a maximum probability 1 (cid:1) k of being re-identified.”
- Airavat: Security and Privacy for MapReduce (2010, background): “…syntactically indistinguishable from at least k − 1 other records on the so-called quasi-identifying attributes, such as ZIP code and date of birth [7, 48]. k -anonymity is achieved by syntactic generalization and suppression of these attributes ( e.g. , [31]). k -anonymity does not provide…”
- A General Survey of Privacy-Preserving Data Mining Models and Algorithms (2008, background): “A good overview and survey of the corresponding algorithms may be found in [31].”