Section: New Results

Minimal Exposure

Participants : Nicolas Anciaux, Walid Bezza, Danae Boutara, Benjamin Nguyen, Michalis Vazirgiannis.

When users request a service, the service provider usually asks for personal documents to tailor its service to the specific situation of the applicant. For example, the rate and duration of consumer’s loans are usually adapted depending on the risk based on the income, assets or past lines of credits of the borrower. In practice, an excessive amount of personal data is collected and stored. Indeed, a paradox is at the root of this problem: service providers require users to expose data in order to determine whether that data is needed or not to achieve the purpose of the service. We explore a reverse approach, where service providers would publicly describe the data they require to complete their task, and where software (placed, depending on the context, on the client, on the server, or in a trusted hardware component) would use those descriptions to determine a minimum subset of information to expose. In 2012, we have presented our general framework called Minimum Exposure [14] , we have modelled the underlying problem (for simple tasks) and proposed resolution algorithms [19] , [24] , and we have addressed the case of multi-label classifiers [18] . In the short term, we plan to adapt the minimum exposure architecture to support hidden decision rules using smart cards. Then, we will investigate new privacy metrics to capture the degree of exposure of sets of personal data items better.