Modern technologies are improving the way how human beings monitor the surrounding environment and, in fact, their own health conditions. This paper proposes an innovative user-centric framework to quantify the privacy level offered by medical awareness devices. Trying to address the well-known privacy issue about personal data collection without user understanding the real risks, the framework proposes a privacy quantification based on: user preferences (users have the right to decide about their privacy); context (scenario and system analysis); and privacy policy analysis. By employing Natural Language Processing (NLP) semantic analysis, our proof-of-concept was able to outperform state-of-the-art models for classification of Privacy Policies, within a well known dataset. The main benefits of the proposed framework are (i) improved informed consent, (ii) users’ possibility to define privacy preferences, and (iii) a system analysis not only focused on privacy policies. © 2023 IEEE.
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