Achieving k-Anonymity Privacy Protection Using Generalization and Suppression

By Latanya Sweeney. 2002

2,146 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Privacy Preserving Data Mining (2015, neutral): “Samarati (2001) and Sweeney (2002).”
Interactive machine learning for health informatics: when do we need the human-in-the-loop? (2016, background): “…k-anonymity means that attributes are suppressed or generalized until each row in a database is identical with at least k (cid:4) 1 other rows [40, 41]; l-diversity as extension of the k-anonymity model reduces the granularity of data representation by generalization and suppression so that any…”
Data privacy through optimal k-anonymization (2005, background): “While there are several -anonymization algorithm proposals in the literature [5] [6] [8] [10] [12] [13] [16], only a few are suitable for use in practice.”
Preserving the Privacy of Sensitive Relationships in Graph Data (2007, methodology): “Section 8 presents the benefits and disadvantages of each anonymization method in an experimental setting, and Section 9 contains concluding remarks and ideas for future work. resent records, and the columns represent attributes [1] [3] [8] [9] [12] [18].”
Privacy, accuracy, and consistency too: a holistic solution to contingency table release (2007, background): “For example, k-anonymity[35, 37, 36] and its extension l-diversity[32] impose syntactic constraints on the outputs, requiring that”
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]).”
A Survey of Inference Control Methods for Privacy-Preserving Data Mining (2008, background): “A different approach to facing the conflict between information loss and disclosure risk is suggested by Samarati and Sweeney [67, 66, 73, 74].”
Anonymity-preserving data collection (2005, background): “In contrast to our setting, work on k -anonymization [32, 31, 30, 7] deals with the complementary problem of ensuring that data does not reveal the identity of the data subject, by processing a database table to de-associate privacy-sensitive attributes from the corresponding identi(cid:12)ers.”
Data and Structural k-Anonymity in Social Networks (2009, methodology): “The method we use for anonymizing attribute data is generalization [22,25].”
Ordinal, Continuous and Heterogeneous k-Anonymity Through Microaggregation (2005, background): “A different approach to facing the conflict between information loss and disclosure risk is the following concept proposed by Samarati and Sweeney (1998), Samarati (2001) and Sweeney (2002a, 2002b).”
Computing with Spatial Trajectories (2011, methodology): “After several works and development of some ad-hoc solutions [89], the field of Moving Objects Databases (MOD) [56, 163] emerged in the late 1990’s as an enabling technology for the LBS-related applications, providing formal foundations and bringing about development of prototype systems [51, 69].”