Main Article Content
Personal Data Storage (PDS) has inau-gurated a significant change to the way in which people can store and con-troltheir owndata in recent years, by moving from administration driven to a client drivenmodel. Till now, most of the research on PDS has focused on the most proficient method to authorized user privacypreferences and how to se-cure data whenput away into the PDS. Conversely, the main point of interest of assisting clients with indicatingtheir privacy inclinations on PDS infor-mation has not been sofar deeply inves-tigated.Conversely, in this paper we target planning a Privacy-careful Per-sonal Data Storage (P-PDS), that is, a PDS prepared to normally take security careful decisions onoutsid-er’saccessrequests in accordancewith client inclinations. The proposed P-PDS is depends on preliminary results, where it has been demonstrated that semi-supervised learning can be suc-cessfully exploited to make a PDS ready to consequently choose whether an entrance demand must be approved ornot.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.