Incognito: efficient full-domain K-anonymity

By Kristen LeFevre, David J. DeWitt, Raghu Ramakrishnan. ACM SIGMOD Conference, 2005

1,292 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/e226aabb364119719f7879ebd828e28e812e1b36

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In Artificial Intelligence

Citations in the corpus

On static and dynamic methods for condensation-based privacy-preserving data mining (2008, background): “A number of papers have also appeared on the k -anonymity model [8; 7; 19; 17; 18; 23; 29; 38; 40] recently.”
δ-Presence without Complete World Knowledge (2010, result): “The difference between these approaches is analogous to the difference between the k-anonymization algorithms of [14] and [15].”
Aggregate Query Answering on Anonymized Tables (2007, background): “In the Incognito approach of [10], generalization hierarchies are explored in a vertical way.”
Context-Aware Generative Adversarial Privacy (2017, background): “Other annoymization techniques, such as generalization [11, 32, 49] and suppression [41, 68, 86], also cannot prevent an adversary from performing the sensitive linkages or recover private information from published datasets [31].”
Achieving anonymity via clustering (2006, background): “There has been a lot of recent work on kanonymizing a given database table [3, 12].”
Anonymizing Classification Data for Privacy Preservation (2007, background): “Scalable algorithms (with the exponential complexity, the worst case) for finding the optimal k-anonymization were studied in [2], [6], [7], and [16].”
Privacy-preserving data publishing: A survey of recent developments (2010, methodology): “Machanavajjhala et al. [2006, 2007] modi.ed the bottom-up Incognito [LeFevre et al. 2005] to identify an optimal i-diverse table.”
Protecting privacy when mining and sharing user data (2012, background): “One in particular, k-anonymity, has been popular in relation to user data [3,119] until di↵erential privacy was introduced.”
The new Casper: query processing for location services without compromising privacy (2006, methodology): “The k -anonymity model [39, 40] has been widely used in maintaining data privacy (e.g., see [7, 29, 30, 31]).”
Mondrian Multidimensional K-Anonymity (2006, background): “Numerous recoding models have been proposed in the literature for k-anonymization [8] [9] [13] [17] [10] Figure 1.”
Preserving User Location Privacy in Mobile Data Management Infrastructures (2006, background): “Recent work in k-anonymity include [13], which presents efficient query algorithms for a k-anonymity model; and [14], which uses k-anonymity for privacy and copyright protection.”
Differential Privacy in Telco Big Data Platform (2015, methodology): “To avoid the attack using quasi-identiﬁers, K -Anonymity [29, 5, 15] is invented to provide stronger privacy protection, which ensures that any quasi-identiﬁer in the published dataset appears at least K times.”
M-invariance: towards privacy preserving re-publication of dynamic datasets (2007, methodology): “The QI-groups output by our algorithm conform to the localrecoding generalization scheme [12], in contrast to the globalrecoding scheme often adopted in the literature.”
Minimality Attack in Privacy Preserving Data Publishing (2007, methodology): “We used a configuration similar to [10, 12].”