Luc Rocher

TL;DR: A generative copula-based method that can accurately estimate the likelihood of a specific person to be correctly re-identified, even in a heavily incomplete dataset, casting doubt on the adequacy of current anonymization practices.

TL;DR: Researchers have compared the recent availability of large-scale behavioral datasets, such as the ones generated by mobile phones, to the invention of the microscope, giving rise to the new field of computational social science.

TL;DR: Bandicoot makes it easy for machine learning researchers and practitioners to load mobile phone data, to analyze and visualize them, and to extract robust features which can be used for various classification and clustering tasks.

TL;DR: There is strong evidence that the tool used historically to find a balance between using the data in aggregate and protecting people’s privacy, de-identification, does not scale to big data datasets.

TL;DR: In this paper, the authors present a new class of noise-exploitation attacks, exploiting the noise added by the system to infer private information about individuals in the dataset, and demonstrate that adding data-dependent noise, as done by Diffix, is not sufficient to prevent inference of private attributes.