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Pain-Free Random Differential Privacy with Sensitivity Sampling
Benjamin Rubinstein · Francesco Aldà

Wed Aug 09 01:30 AM -- 05:00 AM (PDT) @ Gallery #77

Popular approaches to differential privacy, such as the Laplace and exponential mechanisms, calibrate randomised smoothing through global sensitivity of the target non-private function. Bounding such sensitivity is often a prohibitively complex analytic calculation. As an alternative, we propose a straightforward sampler for estimating sensitivity of non-private mechanisms. Since our sensitivity estimates hold with high probability, any mechanism that would be (epsilon,delta)-differentially private under bounded global sensitivity automatically achieves (epsilon,delta,gamma)-random differential privacy (Hall et al. 2012), without any target-specific calculations required. We demonstrate on worked example learners how our usable approach adopts a naturally-relaxed privacy guarantee, while achieving more accurate releases even for non-private functions that are black-box computer programs.

Author Information

Benjamin Rubinstein (University​ of Melbourne)

Ben joined the University of Melbourne in 2013 as a Senior Lecturer in Computing and Information Systems. Previously he gained four years of industry experience in the research divisions of Microsoft, Google, Intel, Yahoo!, IBM. He has shipped production systems for entity resolution in Bing and the Xbox, identify and plug side-channel attacks against the popular Firefox browser, and [deanonymise](http://www.health.gov.au/internet/main/publishing.nsf/Content/mr-yr16-dept-dept005.htm) an unprecedented Australian Medicare data release, prompting introduction of the [Re-identification Offence Bill 2016](http://www.smh.com.au/national/public-service/can-the-government-really-protect-your-privacy-when-it-deidentifies-public-data-20161204-gt3nny.html). He actively researches topics in machine learning, security & privacy, databases such as adversarial learning, differential privacy and record linkage. Ben earned a PhD from UC Berkeley under Peter Bartlett in 2010.

Francesco Aldà (Ruhr-Universität Bochum)

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