The past four years have witnessed the rapid development of federated learning (FL).
However, new privacy concerns have also emerged during the aggregation of the …

Big data has fundamentally changed the way organizations manage, analyze and leverage
data in any industry. One of the most promising fields where big data can be applied to make …

The goal of privacy metrics is to measure the degree of privacy enjoyed by users in a system
and the amount of protection offered by privacy-enhancing technologies. In this way, privacy …

The ever-increasing integration of highly diverse enabled data generating technologies in
medical, biomedical and healthcare fields and the growing availability of data at the central …

We continue a line of research initiated in [10, 11] on privacy-preserving statistical
databases. Consider a trusted server that holds a database of sensitive information. Given a …

In 1977 Dalenius articulated a desideratum for statistical databases: nothing about an
individual should be learnable from the database that cannot be learned without access to …

Publishing data about individuals without revealing sensitive information about them is an
important problem. In recent years, a new definition of privacy called k-anonymity has …

Over the past five years a new approach to privacy-preserving data analysis has born fruit
[13, 18, 7, 19, 5, 37, 35, 8, 32]. This approach differs from much (but not all!) of the related …

Calibrating Noise to Sensitivity in Private Data Analysis Page 1 Journal of Privacy and
Confidentiality (2016-2017) 7, Number 3, 17–51 Calibrating Noise to Sensitivity in Private Data …

We present a new class of statistical de-anonymization attacks against high-dimensional
micro-data, such as individual preferences, recommendations, transaction records and so …