Privacy: Theory meets Practice on the Map

By Ashwin Machanavajjhala, Daniel Kifer, John M. Abowd, Johannes Gehrke, Lars Vilhuber. 2008

663 citations recorded by the source corpus.

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

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Citations in the corpus

Fair decision making using privacy-protected data (2019, background): “ividuals equally? Or are some populations systematically disadvantaged as a result of privacy technology? These questions are especially important now: the adoption of differential privacy is growing [13, 14, 16, 21], and, in particular, the U.S. Census Bureau is currently exploring differentially p”
BLENDER: Enabling Local Search with a Hybrid Differential Privacy Model (2017, background): “Differential privacy [10, 9, 11], which has become the gold standard privacy guarantee in the academic literature, and is gaining traction in industry and government [13, 17, 28], overcomes the prior issues by focusing on the privatization algorithm applied to the data, requiring that it preserves p”
Knock Knock, Who's There? Membership Inference on Aggregate Location Data (2017, background): “[19], who use synthetic data generation to release differentially private mobility patterns of commuters in Minnesota.”
Advanced Probabilistic Couplings for Differential Privacy (2016, neutral): “Other tools implement algorithms targeting speciﬁc applications like: location data [35], genomic data [27, 47], mobility data [37], and browser error reports [26].”
Signal Processing and Machine Learning with Differential Privacy (2013, background): “0 d = Variants of ( , ) e d differential privacy such as ( , , ) 1 d e indistinguishability [7] and dprobabilistic privacy [32] have also been considered in the literature; we focus on the most popular variant for our purpose.”
Differentially Private Empirical Risk Minimization (2009, neutral): “(2006b), and has been used since in numerous works on privacy (Chaudhuri and Mishra, 2006; McSherry and Talwar, 2007; Nissim et al., 2007; Barak et al., 2007; Chaudhuri and Monteleoni, 2008; Machanavajjhala et al., 2008).”
The U.S. Census Bureau Adopts Differential Privacy (2018, background): “Problem statement: Designing and engineering production differential privacy systems requires two primary components: (1) inventing and constructing algorithms that deliver maximum accuracy for a given privacyloss budget and (2) insuring that the privacy-loss budget can be directly controlled by the”
Differential Privacy: An Economic Method for Choosing Epsilon (2014, background): “This is evident in the literature [32, 7, 30, 24, 5, 31, 6, 28, 38, 8, 2, 41, 43, 27, 9, 12, 40, etc.], where algorithms have been evaluated with e ranging from as little as 0.01 to as much as 7, often with no explanation or justiﬁcation.”
Utility Cost of Formal Privacy for Releasing National Employer-Employee Statistics (2017, methodology): “In prior work [37], we developed an algorithm to protect employee residence locations that provably ensures probabilistic differential privacy.”
What Does The Crowd Say About You? Evaluating Aggregation-based Location Privacy (2017, methodology): “[28] use synthetic data generation techniques to publish commuting patterns in a differentially private way, while Acs and Castelluccia [2] describe a differentially private scheme to release the spatio-temporal density of Paris regions using records provided by a telco operator.”
Membership privacy: a unifying framework for privacy definitions (2013, background): “[25] introduced a variant of (ǫ, δ)-differential privacy called (ǫ, δ)-probabilistic differential privacy.”
Generalized Gaussian Mechanism for Differential Privacy (2016, background): “In addition to the “pure” -DP in Eqn (1), there are softer versions of DP, including the ( , δ)approximate DP (aDP) [13], the ( , δ)-probabilistic DP (pDP) [14], the ( , δ)-random DP (rDP) [15], and the ( , τ)-concentrated DP (cDP) [16].”