Pain-Free Random Differential Privacy with Sensitivity Sampling

By Benjamin I. P. Rubinstein, Francesco Aldà. 2017

45 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Sociology and Political Science

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Eugenio Martínez-Cámara
2. M. V. Luzón
3. Daniel Jiménez-López
4. José Antonio Ruiz-Millán
5. Gerardo González-Seco
6. G. Stipcich
7. Francisco Herrera
8. M. Veganzones

Citations in the corpus

• Federated Learning and Differential Privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacy (2020, methodology): “1 The Laplace mechanism is used when the model’s sensitivity is estimated by a sampling procedure [19].”