Team, Visitors, External Collaborators
Overall Objectives
Application Domains
Highlights of the Year
New Software and Platforms
New Results
Partnerships and Cooperations
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Section: New Results

Analysis of privacy policies to enhance informed consent

Participant : Daniel Le Métayer.

A privacy policy language must meet a number of requirements to be able to express the valid consent of the data subject for the processing of their personal data. For example, under the GDPR, valid consent must be freely given, specific, informed and unambiguous. Therefore, the language must be endowed with a formal semantics in order to avoid any ambiguity about the meaning of a privacy policy. However, the mere existence of a semantics does not imply that DSs properly understand the meaning of a policy and its potential consequences. One way to enhance the understanding of the data subjects is to provide them information about the potential risks related to a privacy policy. This is in line with Recital 39 of the GDPR which stipulates that data subjects should be “made aware of the risks, rules, safeguards and rights in relation to the processing of personal data and how to exercise their rights in relation to such processing”. To address this need, we have defined a language in [11], called PILOT, meeting these requirements and shown its benefits to define precise privacy policies and to highlight the associated privacy risks. In order to automatically answer questions related to privacy risks, we use the verification tool SPIN and the modeling language PROMELA. Risk properties are encoded in Linear Temporal Logic properties that can be automatically checked by SPIN.