Descripción del proyecto
Más seguridad para los datos durante la recogida de datos estadísticos
La cantidad de datos personales que se recogen se ha multiplicado. Para las empresas, las mejoras en la recogida de estos datos aumentan la calidad de los servicios ofrecidos. Sin embargo, todo ello despierta inquietudes considerables relativas a la privacidad. El proyecto financiado con fondos europeos HYPATIA contribuye a desarrollar los fundamentos teóricos, los métodos y las herramientas con los que proteger la privacidad de los ciudadanos y, al mismo tiempo, permitir que puedan recogerse y aprovecharse con fines estadísticos. Sus responsables también trabajan en un marco para la recopilación de datos. Su plan es añadir ruido controlado a los datos personales a fin de proteger la privacidad y lograr métodos asociados de recuperación de información estadística útil sin menoscabo de la calidad del servicio.
Objetivo
With the ever-increasing use of internet-connected devices, such as computers, smart grids, IoT appliances and GPS-enabled equipments, personal data are collected in larger and larger amounts, and then stored and manipulated for the most diverse purposes. Undeniably, the big-data technology provides enormous benefits to industry, individuals and society, ranging from improving business strategies and boosting quality of service to enhancing scientific progress. On the other hand, however, the collection and manipulation of personal data raises alarming privacy issues. Both the experts and the population at large are becoming increasingly aware of the risks, due to the repeated cases of violations and leaks that keep hitting the headlines. The objective of this project is to develop the theoretical foundations, methods and tools to protect the privacy of the individuals while letting their data to be collected and used for statistical purposes. We aim in particular at developing mechanisms that: (1) can be applied and controlled directly by the user, thus avoiding the need of a trusted party, (2) are robust with respect to combination of information from different sources, and (3) provide an optimal trade-off between privacy and utility. We intend to pursue these goals by developing a new framework for privacy based on the addition of controlled noise to individual data, and associated methods to recover the useful statistical information, and to protect the quality of service.
Programa(s)
Régimen de financiación
ERC-ADG - Advanced GrantInstitución de acogida
78153 Le Chesnay Cedex
Francia