Crowd-Blending Privacy

By Johannes Gehrke, Michael Hay, Edward Lui, Rafael Pass. 2012

98 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Science Applications

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Citations in the corpus

Federated Heavy Hitters Discovery with Differential Privacy (2019, background): “The connection between DP, random sampling, and k-anonymity has previously appeared in the literature [13, 36, 28].”
Pufferfish (2014, background): “P uffer F ish( S , pairs ) satisﬁes the axioms of convexity and transformation invariance.”
Coupled-Worlds Privacy: Exploiting Adversarial Uncertainty in Statistical Data Privacy (2013, background): “[10] on truncated histograms; we explain the relationships between the results further below.”
Membership privacy: a unifying framework for privacy definitions (2013, methodology): “We combine these two requirements and introduce a novel privacy framework that we call Membership Privacy.”
Discrimination- and privacy-aware patterns (2015, background): “…control the trade-off between privacy and quality of resulting data or models. k-Anonymity (Samarati 2001; Sweeney 2002, l-diversity (Machanavajjhala et al. 2007), t-closeness (Li et al. 2007), differential privacy (Dwork 2006) and crowd-blending Gehrke et al. (2012) are examples of such models.”
Stadium: A Distributed Metadata-Private Messaging System (2017, background): “As suggested in [2, 5, 24], Stadium can benefit from using legitimate users’ traffic as noise.”
Privacy-Preserving Bandits (2019, methodology): “We refer the interested reader to the rigorous analysis and proofs provided in the original paper (Gehrke et al., 2012).”
Semantic Security: Privacy Definitions Revisited (2013, background): “( -blend in a crowd of k instances in D)[11].”