Mondrian Multidimensional K-Anonymity

By Kristen LeFevre, David J. DeWitt, Raghu Ramakrishnan. IEEE International Conference on Data Engineering, 2006

1,271 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/065a0a69f4dd506b7ee457da13d6f7e6fb1b043e

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In Artificial Intelligence

Citations in the corpus

Hiding the presence of individuals from shared databases (2007, background): “Mondrian multidimensional k-anonymity.”
Data and Structural k-Anonymity in Social Networks (2009, methodology): “To measure the amount of information loss, several cost measures were introduced [4,7,13].”
Fast Data Anonymization with Low Information Loss (2007, methodology): “In the following experiments, we compare our 1-D optimal k-anonymity algorithm against the existing state-ofthe-art techniques: The multi-dimensional (Mondrian) kanonymity [10], and the TopDown clustering-based technique [21] (see Section 2 for details).”
Application of Local Differential Privacy to Collection of Indoor Positioning Data (2018, background): “Mondrian [16] is a multidimensional generalization model that anonymizes data by recursively partitioning the space across the dimen-sion.”
Anatomy: simple and effective privacy preservation (2006, methodology): “The actual pdf G t and approximate ˜ G DZt are given in Equations 9 and 11, respectively.”
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]).”
Anonymizing Social Networks (2007, background): “There has been considerable follow-on work on algorithms for constructing k-anonymous tables [6, 12]; on improving the utility of the anonymized data [11, 13]; and on subtler aspects of disclosure, such as inferring properties of the target even without perfect re-identification [16, 17].”
t-Closeness: Privacy Beyond k-Anonymity and l-Diversity (2007, background): “Numerous algorithms [2, 6, 11, 10, 16, 17] have been proposed to achieve k-anonymity requirement.”
Anonymizing Classification Data for Privacy Preservation (2007, background): “LeFevre et al. [17] proposed the notion of multidimensional k-anonymity, where data generalization is over multidimension-at-a-time, and LeFevre et al. [18] extended multidimensional generalization to anonymize data for a specific task such as classification.”
Differentially private data release for data mining (2011, background): “algorithms [2, 13, 24, 23, 36], tailored for both general and specific data mining tasks, have been proposed based on generalization.”
k -Anonymous Data Mining: A Survey (2008, methodology): “Incognito, proposed by LeFevre, DeWitt and Ramakrishnan [18], is an algorithm for AG TS based on the observation that k-anonymity with respect to any subset of QI is a necessary (not sufficient) condition for k-anonymity with respect to QI.”
Protecting privacy using k-anonymity. (2008, methodology): “44,86–91 In Appendix A (Risk Estimates) we describe how this cost function is adjusted to implement the approaches D2, D3, and D4 within the same global optimization algorithm.”
Never Walk Alone: Uncertainty for Anonymity in Moving Objects Databases (2008, background): “This problem is partially solved by the hierarchy-free multidimensional approach proposed in [10], that however has the main limitation of introducing inconsistency in the domain.”