Pufferfish

By Daniel Kifer, Ashwin Machanavajjhala. 2014

198 citations recorded by the source corpus.

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

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Citations in the corpus

Privacy in Deep Learning: A Survey (2020, background): “A generalized version of differential privacy called Pufferfish was proposed by [62].”
Differential Privacy in the Wild: A Tutorial on Current Practices & Open Challenges (2016, background): “We will discuss the No Free Lunch Theorem in data privacy [25], and present alternate privacy definitions that can be customized to match the structure of the data [16, 21, 26].”
Revisiting Differentially Private Hypothesis Tests for Categorical Data (2015, background): “For additional interpretations of the privacy guarantees of differential privacy and suggestions for setting the privacy parameter , see Dwork (2006), Kifer and Machanavajjhala (2014) and Machanavajjhala and Kifer (2015).”
Pufferfish Privacy Mechanisms for Correlated Data (2016, background): “The privacy framework of our choice is Puﬀerﬁsh [20], an elegant generalization of diﬀerential privacy [8].”
Enabling Privacy-Preserving GWAS in Heterogeneous Human Populations (2016, unknown): “…one coordinate, and for all sets S 3 U, we have PðFðD; yÞ˛SÞ%expðEÞPðFðD; y0Þ˛SÞ: This definition of privacy can be viewed as a specific instantiation of both induced differential privacy (Kifer andMachanavajjhala, 2011) and the Blowfish framework (Kifer and Machanavajjhala, 2014; Hi et al., 2014).”
DEEProtect: Enabling Inference-based Access Control on Mobile Sensing Applications (2017, background): “4 in [44]) and Blowfish privacy (Definition 4.”
Privacy-preserving Analysis of Correlated Data (2016, methodology): “However, while a number of Pufferfish mechanisms for specific frameworks are known (Kifer and Machanavajjhala, 2014; He et al., 2014), there is no generic mechanism that applies to any Pufferfish framework.”
Correlated Differential Privacy: Hiding Information in Non-IID Data Set (2015, background): “The Exponential mechanism is formally defined as [8]: Definition 4 (Exponential Mechanism): An Exponential mechanism M is -differential privacy if: M(D) = {return ψ with the probability ∝ exp( Q(D, ψ) 2GS )}, C. Related Work 1) Correlated Differential Privacy: In the past decade, a growing body of…”
Bayesian Modeling of Intersectional Fairness: The Variance of Bias (2018, methodology): “We use the notation of [19] for all definitions we consider.”
LightDP: towards automating differential privacy proofs (2016, background): “…answer queries, and build statistical models satisfy differential privacy (Dwork et al. 2006b) — a set of restrictions on their probabilistic behavior that provably limit the ability of attackers to infer individual-level sensitive information (Dwork et al. 2006b; Kifer and Machanavajjhala 2014).”