t-Closeness: Privacy Beyond k-Anonymity and l-Diversity

By Ninghui Li, Tiancheng Li, Suresh Venkatasubramanian. 2007

3,755 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Sociology and Political Science, Privacy & Security

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Citations in the corpus

Marginal Release Under Local Differential Privacy (2017, background): “The model has risen in popularity and adoption compared to earlier attempts to codify privacy (such as k-anonymity and l-diversity [30, 37]), which can leak sensitive information.”
Dependence Makes You Vulnberable: Differential Privacy Under Dependent Tuples (2016, background): “Data privacy is an issue of critical importance, motivating perturbation of query results over sensitive datasets for protecting users’ privacy [5], [12], [26], [29], [31], [38].”
Data Synthesis based on Generative Adversarial Networks (2018, background): “Consequently, the authors of [22] introduced t-closeness to ensure that the distributions of sensitive attributes in each equivalence class are similar to their global distributions.”
Composition attacks and auxiliary information in data privacy (2008, methodology): “For l-diversity andt-closeness, we use the definitions of entropy l-diversity andt-closeness proposed in [26] and [23], respectively.”
Interactive machine learning for health informatics: when do we need the human-in-the-loop? (2016, background): “…a data representation, and treating the values of an attribute distinctly by taking into account the distribution of data values for that attribute [43]; and delta-presence , which links the quality of anonymization to the risk posed by inadequate anonymization [44]), but not with regard to the…”
Differentially Private Hierarchical Count-of-Counts Histograms (2018, neutral): “Lemma 1 ([27]).”
Federated Multi-view Learning for Private Medical Data Integration and Analysis (2021, background): “Most of the research is centered around data de-identification [16, 17, 50, 53] and data anonymization [14, 39, 43, 55], which removes the identifiable information from the published medical data to prevent an adversary from reasoning about the patients’ privacy.”
Big healthcare data: preserving security and privacy (2018, methodology): “This model (Distinct, Entropy, Recursive) [46, 47, 51] is an extension of the k-anonymity which utilizes methods including generalization and suppression to reduce the granularity of data representation in a way that any given record maps onto at least k different records in the data.”
Big data privacy: a technological perspective and review (2016, methodology): “K‐anonymity A release of data is said to have the k-anonymity [29, 31] property if the information for each person contained in the release cannot be perceived from at least k-1 individuals whose information show up in the release.”
Optimized Data Pre-Processing for Discrimination Prevention (2017, methodology): “Existing pre-processing approaches include sampling or re-weighting the data to neutralize discriminatory eﬀects (Kamiran & Calders, 2012), changing the individual data records (Hajian & Domingo-Ferrer, 2013), and using t -closeness (Li et al., 2007) for discrimination control (Ruggieri, 2014).”
BBDS: Blockchain-Based Data Sharing for Electronic Medical Records in Cloud Environments (2017, neutral): “Techniques like K-anonymity [11], `-Diversity [12], t-closeness [13,14], and others developed to guarantee privacy in such instances have not been sufficient.”
Differential Privacy Techniques for Cyber Physical Systems: A Survey (2018, methodology): “The data clustering techniques initiated from k-anonymity [16], to l-diversity [49], and then to t-closeness methods [50], [51].”
Resisting structural re-identification in anonymized social networks (2008, background): “In tabular data, definitions such as -diversity [31] and t-closeness [29] have been introduced to counter the threat of homogeneity attacks.”
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.”