Anatomy: simple and effective privacy preservation

By Xiaokui Xiao, Yufei Tao. Very Large Data Bases Conference, 2006

829 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/39f514e4a6bfb0b0ac3de5f99787eaa2b96b1d09

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Citations in the corpus

Minimality Attack in Privacy Preserving Data Publishing (2007, methodology): “Recently [23] proposed to generate two separate tables from T with the introduction of an attribute called GID that is shared by the two tables.”
Personal privacy vs population privacy: learning to attack anonymization (2011, unknown): “Anatomy is a grouping-based anonymization method [22].”
On the tradeoff between privacy and utility in data publishing (2009, methodology): “, how much an adversary can learn about the sensitive value of an individual from the individual’s QI values) while utility [30] is measured by attribute correlations be-”
Worst-Case Background Knowledge for Privacy-Preserving Data Publishing (2007, methodology): “Anatomy [34] is a recently proposed anonymization technique that corresponds exactly to the notion of bucketization that we use in this paper.”
Privacy: Theory meets Practice on the Map (2008, methodology): “Although much privacy research has focused on answering range queries [20], [9], [14], [21], in contrast to this work, we decided to evaluate the quality of the data using a measure for which we did not explicitly tune our anonymization algorithm.”
Composition attacks and auxiliary information in data privacy (2008, background): “Partition-based schemes include k-anonymity [33] as well as several recent variants, e.g., [28, 39 , 40, 25, 41, 29, 10, 23].”
t-Closeness: Privacy Beyond k-Anonymity and l-Diversity (2007, background): “Xiao and Tao [20] proposed Anatomy, an data anonymization approach that divides one table into two for release; one table includes original quasi-identifier and a group id, and the other include the association between the group id and the sensitive attribute values.”
Privacy-preserving data publishing: A survey of recent developments (2010, methodology): “Anatomy [Xiao and Tao 2006a] AM heuristics minimal”
M-invariance: towards privacy preserving re-publication of dynamic datasets (2007, background): “Disease 1 [21, 22] [12k, 14k] dyspepsia 1 [21, 22] [12k, 14k] bronchitis 2 [23, 24] [18k, 25k] flu 2 [23, 24] [18k, 25k] gastritis 3 [36, 41] [20k, 27k] flu 3 [36, 41] [20k, 27k] gastritis 4 [37, 43] [26k, 35k] dyspepsia 4 [37, 43] [26k, 35k] flu 4 [37, 43] [26k, 35k] gastritis 5 [52, 56] [33k, 34k]”
A rigorous and customizable framework for privacy (2012, unknown): “[6, 23, 40].”
δ-Presence without Complete World Knowledge (2010, background): “Works such as [7], [13] also assume the same adversary model as in k-anonymity, but distort the link between sensitive attribute and quasi identifiers as opposed to modifying identifiers.”
K-Automorphism: A General Framework For Privacy Preserving Network Publication (2009, background): “Obviously, if G is a k -automorphic network, for any vertex v in G , we cannot distinguish v from its k − 1 symmetric vertices based on any structural information.”
Aggregate Query Answering on Anonymized Tables (2007, background): “[13] propose to achieve kanonymity by separating quasi-identifiers and sensitive attributes into two tables, connected by the group ID of each tuple.”
Anti-discrimination Analysis Using Privacy Attack Strategies (2014, methodology): “We investigate the scenario for one of the most popular ones, the bucketization method [15].”
Slicing: A New Approach to Privacy Preserving Data Publishing (2009, methodology): “It has been shown [1, 15, 35] that generalization for kanonymity losses considerable amount of information, especially for high-dimensional data.”
K-isomorphism: privacy preserving network publication against structural attacks (2010, background): “The first shortfall of k-anonymity in addressing our problem comes from the same issue that has been identified by [28] which proposes”