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DTSTART;TZID=Europe/Paris:20220310T150000
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DTSTAMP:20220309T155742Z
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SUMMARY:PhD defense: Bizhan Alipour Pijani (Pesto)
DESCRIPTION:Bizhan Alipour Pijani (Pesto) will defend his thesis on Thursday\, March 10\, 2022 at 3 pm  in Room A006. \nHis presentation is entitled “Attribute Inference Attacks on Social Medias Publications”. \nAbstract: The privacy settings available in Online Social Networks (OSN)\n\n\n\n\ndo not prevent users from attribute inference attacks where an attacker seeks to illegitimately ob-\ntain their personal attributes (such as gender) from publicly available information.\nDisclosure of personal information can have serious outcomes such as personal spam\, bullying\,\nprofile cloning for malicious activities\, or sexual harassment. Existing inference techniques are\neither based on the target user behavior analysis through their liked pages and group member-\nships or based on the target user friend list. However\, in real cases\, the amount of available\ninformation to an attacker is small since users have realized the vulnerability of standard at-\ntribute inference attacks and concealed their generated information. To increase awareness of\nOSN users about threats to their privacy\, in this thesis\, we introduce a new class of attribute\ninference attacks against OSN users. We show the feasibility of these attacks from a very limited\namount of data. They are applicable even when users hide all their profile information and their\nown comments. Our proposed methodology is to analyze Facebook picture metadata\, namely\n(i) alt-text generated by Facebook to describe picture contents\, and (ii) commenters’ words and\nemojis preferences while commenting underneath the picture\, to infer sensitive attributes of the\npicture owner. We show how to launch these inference attacks on any Facebook user by i) han-\ndling online newly discovered vocabulary using a retrofitting process to enrich a core vocabulary\nthat was built during offline training and ii) computing several embeddings for textual units\n(e.g.\, word\, emoji)\, each one depending on a specific attribute value. Finally\, we introduce a\nprotection mechanism that selects comments to be hidden in a computationally efficient way\nwhile minimizing utility loss according to a semantic measure. The proposed mechanism can\nhelp end-users to check their vulnerability to inference attacks and suggests comments to be\nhidden in order to mitigate the attacks. We have determined the success of the attacks and the\nprotection mechanism by experiments on real data.
URL:https://www.loria.fr/event/phd-defense-bizhan-alipour-pijani/
CATEGORIES:Soutenance
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