Privacy-preserving data publishing

By Benjamin C. M. Fung. 2007

164 citations recorded by the source corpus.

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Citations in the corpus

A rigorous and customizable framework for privacy (2012, background): “Because of this, the resulting privacy definitions tend to be less secure and are often subject to new kinds of attacks (see the surveys [6, 16, 1] for more details).”
Anti-discrimination Analysis Using Privacy Attack Strategies (2014, background): “We follow the intriguing parallel between the role of the anti-discrimination authority in discrimination data analysis and the role of an attacker in privacypreserving data publishing [1,4,5] – an unauthorized (possibly malicious) entity.”
Blowfish privacy: tuning privacy-utility trade-offs using policies (2013, background): “for the utility (or accuracy) of data analysis (see [4] for a survey).”
Crowd-Blending Privacy (2012, background): “Many privacy definitions and schemes for releasing data have been proposed in the past (see [1] and [2] for surveys).”
Secure multiple linear regression based on homomorphic encryption (2011, methodology): “, Chen et al. (2009). Other approaches include the use of data masking such as the addition of normally distributed noise to the underlying data; cf. Duncan and Pearson (1991) and the recent review by Duncan and Stokes (2009). This leads, in the case of regression analysis, to inference using measur”
Perturbed Gibbs Samplers for Generating Large-Scale Privacy-Safe Synthetic Health Data (2013, background): “A wide variety of privacy oriented mechanisms have been proposed in the past decade, including differential privacy, k-anonymity and l-diversity [10].”
Pufferfish (2014, methodology): “Thus for any individual h i in the population H and any possible tuple value t ∈ T , the i h i is in the data and has value t versus the data has no record about individual h (this is our mathematical translation of the goals in [Dwork 2006]).”