Personalized privacy preservation

By Yufei Tao, Xiaokui Xiao. Privacy-Preserving Data Mining, 2006

747 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/3347b6b9f0696ccdff0e39a391b2ae2e30350db1

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Citations in the corpus

Anonymizing Classification Data for Privacy Preservation (2007, background): “Xiao and Tao [23] limited the breach probability, which is similar to the notion of confidence, and allowed a flexible threshold for each individual. k-anonymization for data owned by multiple parties was considered in [24]. k-anonymization for sequential releases was studied in [25].”
Fast Data Anonymization with Low Information Loss (2007, background): “[18], where each record in the table has an individual privacy constraint.”
Privacy-preserving data publishing: A survey of recent developments (2010, methodology): “…limited to, i-diversity [Machanavajjhala et al. 2007]; (a, k)-anonymity [Wong et al. 2006]; (k, e)-anonymity [Zhang et al. 2007]; personalized privacy [Xiao and Tao 2006b]; anatomy [Xiao and Tao 2006a]; tcloseness [Li et al. 2007]; m-invariance [Xiao and Tao 2007]; and (X, Y )-privacy [Wang…”
Preserving Privacy in Social Networks Against Neighborhood Attacks (2008, background): “[10] proved that l-diversity always guarantees stronger privacy preservation than k-anonymity.”
Privacy: Theory meets Practice on the Map (2008, background): “Many variations on these privacy conditions have also been proposed [33], [21].”
Differentially private data release for data mining (2011, background): “Unlike generalization, bucketization [42, 29] does not modify the QID and the sensitive attribute (SA) values but instead de-associates the relationship between the two.”
Composition attacks and auxiliary information in data privacy (2008, neutral): “, [28, 39, 40, 25, 41, 29, 10, 23].”
PRIVE: anonymous location-based queries in distributed mobile systems (2007, background): “[23] the authors consider the case where each individual requires a diﬀerent degree K of anonymity, while Aggar-wal [1] shows that anonymizing a high-dimensional relation results to unacceptable loss of information due to the dimensionality curse.”
Anatomy: simple and effective privacy preservation (2006, background): “To compute Pr A 2 ( Alice qi ) , an adversary typically needs to consult another external database [17], which relates QI-values to concrete personal identities for all the persons in the microdata, perhaps together with some other people.”
Preventing Location-Based Identity Inference in Anonymous Spatial Queries (2007, background): “Xiao and Tao [31] consider the case where each individual requires a different degree of anonymity, whereas Aggarwal [2] shows that anonymizing a high-dimensional relation leads to unacceptable loss of information due to the dimensionality curse.”
Technical Privacy Metrics (2015, background): “However, studies have shown k-anonymity to be insufficient, especially for high-dimensional data [2] and against correlation with other datasets [86], because it fails to protect against attribute disclosure [138]; i.”
A General Survey of Privacy-Preserving Data Mining Models and Algorithms (2008, methodology): “Therefore, the l -diversity model [83] for privacy is deﬁned as follows: Definition 2.3 Let a q ∗ -block be a set of tuples such that its non-sensitive values generalize to q ∗ .”
Private spatial data aggregation in the local setting (2016, background): “A user’s safe region is speciﬁed as a node in the spatial taxonomy, which is similar to the privacy requirement in [22].”