More and more data related to sensitive areas of human activity are published for statistical and commercial reasons, but also for transparency reasons, especially when public bodies are involved. The publication can be fully open, for example, a website posting, or may be restricted, e.g., within professional associations. In all cases, this gives rise to several issues concerning the preservation of privacy for people described in the data.
Our aim is to develop techniques to ensure privacy for published data, providing anonymity guarantees. We emphasize that simply removing the attributes that directly link the identity of a person with a set of data, for example, the VAT, does not ensure that this connection will remain hidden. The identity of the person can be discovered with the help of external catalogs (such as voter registration lists, telephone directories) that identify a person based on data, e.g., age and place of residence, which in general do not characterize a single individual.
Our research interests focus on sparse multidimensional data, in multiple publications from various sources, spatiotemporal data, as well as life sciences data.