Transforming data to satisfy privacy constraints

By Vijay S. Iyengar. Knowledge Discovery and Data Mining, 2002

908 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/a65f0facd9ef82a89a4bb5d606245767d22ff0a8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

Citations in the corpus

δ-Presence without Complete World Knowledge (2010, methodology): “by the Loss Metric (LM) [18] and the Discernibility Metric”
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.”
Differentially private data release for data mining (2011, methodology): “Bayardo and Agrawal [2] also address the classification problem using the same classification metric of [18].”
A General Survey of Privacy-Preserving Data Mining Models and Algorithms (2008, methodology): “In addition, a number of metrics such as the classification metric [59] explicitly try to perform the privacy-preservation in such a way so as to tailor the results with use for specific applications such as classification.”
A Survey of Quantification of Privacy Preserving Data Mining Algorithms (2008, methodology): “The next metric, classification metric (CM ), is introduced by Iyengar [13] to optimize a k-anonymous dataset for training a classifier.”
Privacy-preserving anonymization of set-valued data (2008, methodology): “The Classiﬁcation Metric ( CM ) [8] is suitable when the purpose of the anonymized data is to train a classi-ﬁer, whereas the Discernibility Metric ( DM ) [4] measures the cardinality of the anonymized groups.”
Privacy-preserving data publishing: A survey of recent developments (2010, methodology): “…Agrawal 2005] SG,RS DM,CM optimal µ-argus [Hundepool and Willenborg 1996] SG,CS MD minimal Data.y [Sweeney 1998] FG,RS DA minimal Genetic Algorithm [Iyengar 2002] SG,RS CM minimal Bottom-Up Generalization [Wang et al. 2004] SG ILPG minimal Top-Down Specialization (TDS) [Fung et al. 2005,…”
Anonymizing Classification Data for Privacy Preservation (2007, methodology): “Greedy methods were proposed in [10], [11], [12], [13], [14], and [15].”
Big healthcare data: preserving security and privacy (2018, background): “Various measures have been proposed to quantify information loss caused by anonymization, but they do not reflect the actual usefulness of data [53, 54].”
Privacy-Preserving Inter-database Operations (2004, background): “Iyengar has addressed the privacy protection problem in a data dissemination environment [19].”
Hiding the presence of individuals from shared databases (2007, methodology): “We evaluate how varying δ affects the cost of anonymizing the dataset, as determined by the Loss Metric (LM) [9] and the Discernibility Metric (DM) [5].”