Project description
Enhanced data protection security with statistical data collection
The volume of personal data collection has surged significantly. For businesses, improved data collection has boosted the quality of services provided. But alarming privacy issues remain for individuals. The EU-funded project HYPATIA helps develop the theoretical foundations, methods and tools to protect the privacy of individuals while allowing their data to be collected and used for statistical purposes. A new framework of data gathering is also being developed under the project. Researchers plan to add controlled noise to individual data for privacy protection and apply associated methods to recover useful statistical information while protecting the quality of service.
Objective
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.
Programme(s)
Topic(s)
Funding Scheme
ERC-ADG - Advanced GrantHost institution
78153 Le Chesnay Cedex
France