Towards an axiomatization of statistical privacy and utility

By Daniel Kifer, Bing-Rong Lin. 2010

111 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

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Citations in the corpus

Dependence Makes You Vulnberable: Differential Privacy Under Dependent Tuples (2016, background): “in [23] suggested two privacy axioms: transformation invariance and convexity that should be satisfied by any consistent privacy definition.”
Differentially private data release for data mining (2011, methodology): “Professional [18-65) Professional [18-65) Professional [18-65) Professional [18-65)”
Impossibility of Differentially Private Universally Optimal Mechanisms (2010, unknown): “6 2.5 Universal Mechanisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7”
Membership privacy: a unifying framework for privacy definitions (2013, background): “Kifer and Lin [18] proposed the privacy axioms of choice and the axiom of transformation, which we show that membership p rivacy satisfy.”
Differentially Private Publication of Sparse Data (2011, methodology): “To release this data in a differentially private way, we add random noise independently to each cell before publishing the matrix.”
Pufferfish (2014, background): “If we choose the data evolution scenarios D other such that D other (cid:7)⊆ D pairs other the odds-ratio to the interval [ e − (cid:6) , e (cid:6) ] .”
A rigorous and customizable framework for privacy (2012, background): “The Pufferfish framework differs from differential privacy [12] and its variants [11, 30, 5, 3, 22, 21, 25, 27, 18, 41, 33] in that it can provide precise guarantees about precisely what is being protected and under what conditions the protections hold.”
On sampling, anonymization, and differential privacy or, k-anonymization meets differential privacy (2011, background): “In [19], Kifer and Lin identi-ﬁed two privacy axioms when they deﬁned the generic differential privacy.”
Differentially private summaries for sparse data (2012, background): “This approach relies on the well-known property that any post-processing of differentially private output remains differentially private [14, 15].”
No free lunch in data privacy (2011, background): “Since then, many additional variations of differential privacy have been proposed [9, 4, 29, 2, 23, 20, 28].”
Blowfish privacy: tuning privacy-utility trade-offs using policies (2013, background): “We discuss variants of differential privacy [6] (including restricted sensitivity [1]), the Pufferfish framework [13], privacy axioms [11], and a recent independent work on extending differential privacy with metrics [3].”
Non-tracking web analytics (2012, background): “The privacy guarantee of the original ResampledNoise follows immediately from the fact that any transformation (in this case, rounding) of a diﬀerentially-private output also preserves privacy [42].”