Minimality Attack in Privacy Preserving Data Publishing

By Raymond Chi-Wing Wong, Ada Wai-Chee Fu, Ke Wang, Jian Pei. Very Large Data Bases Conference, 2007

327 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/45be07dcadbded384b944f7abaa5c1c0cdd77661

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Citations in the corpus

Accurate Estimation of the Degree Distribution of Private Networks (2009, background): “While publishing a graph allows a broader range of analysis, anonymization is a much weaker notion of privacy and is vulnerable to attack (e.g., [6], [ 21 ]).”
Towards an axiomatization of statistical privacy and utility (2010, background): “In other cases, subtle implicit assumptions created weaknesses that could be exploited to breach privacy [25, 41, 22, 27].”
Personal privacy vs population privacy: learning to attack anonymization (2011, background): “INTRODUCTION The topic of anonymization and privacy has occupied the computer science research community for over a decade now (with e.orts in statistics going back even further).”
Secure Distributed Framework for Achieving ε-Differential Privacy (2012, background): “Blue-collar Any_Sex [60-99) ∪Cuti = {Any_Job, Any_Sex, [1-99)} Any_Job {White-collar, Blue-collar} ∪Cuti = {White-collar,Blue-collar, Any_Sex, [1,99)}”
Composition attacks and auxiliary information in data privacy (2008, methodology): “porating knowledge of the clustering algorithm used to grou p individuals [32].”
Anti-discrimination Analysis Using Privacy Attack Strategies (2014, methodology): “The minimality attack [14] is one of the strategies to recover the private data from optimally anonymized data, given the non-sensitive information of individuals in the released dataset, the privacy policy, and the algorithm used for anonymization.”
Differential Privacy in Telco Big Data Platform (2015, background): “Researchers also develop different privacy protection models for other scenarios, such as the graph dataset [20, 37] (the linkage and node label information can be treated as both sensitive and quasi-identiﬁer) and continuously published data [33].”
Differentially private data release for data mining (2011, methodology): “Professional [18-65) Professional [18-65) Professional [18-65) Professional [18-65)”
Towards trajectory anonymization: a generalization-based approach (2008, background): “This approach has the advantage of being resistant against minimality attacks [46].”
The price of privately releasing contingency tables and the spectra of random matrices with correlated rows (2010, background): “, [38, 42, 27, 26, 7, 28, 43]) where one assumes that a database consists of d−1 nonsensitive attributes (e.”
Slicing: A New Approach to Privacy Preserving Data Publishing (2009, background): “Finally, privacy measures for attribute disclosure protection include ℓ -diversity [23], ( α, k )-anonymity [34], t - closeness [20], ( k, e )-anonymity [16], ( c, k )-safety [25], privacy skyline [5], m -conﬁdentiality [33] and ( ǫ, m )- anonymity [19].”
Differential Privacy via Wavelet Transforms (2009, background): “Assumptions about the background knowledge of the adversary determine what types of attacks are possible [3], [4], [5].”
Resisting structural re-identification in anonymized social networks (2008, background): “Attacks include the composition attack [17], the minimality attack [48], and the deFinetti attack [23].”