33 citations · 97 across the 23 of their papers we have counts for
5 papers · 1 filter
Health Data in an Open World
Chris Culnane, Benjamin I. P. Rubinstein, Vanessa Teague
With the aim of informing sound policy about data sharing and privacy, we describe successful re-identification of patients in an Australian de-identified open health dataset. As i…
Vulnerabilities in the use of similarity tables in combination with pseudonymisation to preserve data privacy in the UK Office for National Statistics' Privacy-Preserving Record Linkage
Chris Culnane, Benjamin I. P. Rubinstein, Vanessa Teague
In the course of a survey of privacy-preserving record linkage, we reviewed the approach taken by the UK Office for National Statistics (ONS) as described in their series of report…
Pain-Free Random Differential Privacy with Sensitivity Sampling
Benjamin I. P. Rubinstein, Francesco Aldà
Popular approaches to differential privacy, such as the Laplace and exponential mechanisms, calibrate randomised smoothing through global sensitivity of the target non-private func…
Adequacy of the Gradient-Descent Method for Classifier Evasion Attacks
Yi Han, Benjamin I. P. Rubinstein
Despite the wide use of machine learning in adversarial settings including computer security, recent studies have demonstrated vulnerabilities to evasion attacks---carefully crafte…
End-to-End Differentially-Private Parameter Tuning in Spatial Histograms
Maryam Fanaeepour, Benjamin I. P. Rubinstein
Differentially-private histograms have emerged as a key tool for location privacy. While past mechanisms have included theoretical & experimental analysis, it has recently been obs…