Privacy-preserving data sharing via probabilistic modeling

Differential privacy allows quantifying privacy loss resulting from accession of sensitive personal data. Repeated accesses to underlying data incur increasing loss. Releasing data as privacy-preserving synthetic data would avoid this limitation but would leave open the problem of designing what kind of synthetic data. We propose formulating the problem of private data release through probabilistic modeling. This approach transforms the problem of designing the synthetic data into choosing a model for the data, allowing also the inclusion of prior knowledge, which improves the quality of the synthetic data. We demonstrate empirically, in an epidemiological study, that statistical discoveries can be reliably reproduced from the synthetic data. We expect the method to have broad use in creating high-quality anonymized data twins of key datasets for research.Peer reviewe

First evidence of resistance to pyrethroid insecticides in Italian Aedes albopictus populations after 26 years since invasion

Aedes albopictus has spread during the last decades all over the world. This has increased significantly the risk of exotic arbovirus transmission (e.g. Chikungunya, Dengue, and Zika) also in temperate areas, as testified by the Chikungunya 2007- and 2017-outbreaks in north-east and central Italy. Insecticides represent a main tool for limiting the circulation of these mosquito-borne viruses. The aim of the present study is to start filling the current gap of knowledge on pyrethroid insecticide resistance of European Ae. albopictus populations focusing on populations from Italy, Albania and Greece