Aggregate Query Answering on Anonymized Tables

By Qing Zhang, Nick Koudas, Divesh Srivastava, Ting Yu. IEEE International Conference on Data Engineering, 2007

405 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/323011da81c396825df6ec185ac17a21f2272ea0

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In Artificial Intelligence

Citations in the corpus

t-Closeness: Privacy Beyond k-Anonymity and l-Diversity (2007, background): “[7] examine the anonymization problem from the perspective of answering downstream aggregate queries.”
Pufferfish (2014, neutral): “ELECTRONIC APPENDIX”
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.”
Fast Data Anonymization with Low Information Loss (2007, neutral): “[22] publishes the exact Q T .”
Slicing: A New Approach to Privacy Preserving Data Publishing (2009, background): “h as a person, a household, or an organization. Several microdata anonymization techniques have been proposed. The most popular ones are generalization [29, 31] for k-anonymity [31] and bucketization [35, 25, 16] for ℓ- diversity [23]. In both approaches, attributes are partitioned into three catego”
A General Survey of Privacy-Preserving Data Mining Models and Algorithms (2008, methodology): “In [72], a method has been proposed for anonymization, so that the accuracy of the underlying queries is preserved.”
Privacy-preserving data publishing: A survey of recent developments (2010, background): “2006], (k, e)-anonymity [Zhang et al. 2007], personalized privacy [Xiao and Tao 2006b], anatomy [Xiao and Tao 2006a], t-closeness [Li et al.”
On the tradeoff between privacy and utility in data publishing (2009, methodology): “Bucketization [30, 14, 22] was proposed to remedy this drawback.”
M-invariance: towards privacy preserving re-publication of dynamic datasets (2007, background): “[10] exploit a similar idea for improving the accuracy of aggregate search.”
Privacy-preserving anonymization of set-valued data (2008, methodology): “[21, 23, 5] present various methods to solve the (cid:96) -diversity problem eﬃciently.”
Technical Privacy Metrics (2015, methodology): “o apply to numerical instead of categorical attributes. In addition to the k-anonymity requirement, it requires that the range of sensitive attributes in any equivalence class Emust be greater than e[Zhang et al. 2007b]. priv KE (k;e), where 8E: jEjk^range(E) >e However, (k,e)-anonymity does not ”
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.”