�ݺ�ߣshows by User: EleftheriosSpyromitr

�ݺ�ߣshows by User: EleftheriosSpyromitr / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: EleftheriosSpyromitr / Wed, 14 Sep 2016 09:39:20 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: EleftheriosSpyromitr Perceived versus Actual Predictability of Personal Information in Social Networks /slideshow/perceived-versus-actual-predictability-of-personal-information-in-social-networks/66010082 perceivedversusactual-160914093920
This paper looks at the problem of privacy in the context of Online Social Networks (OSNs). In particular, it examines the predictability of different types of personal information based on OSN data and compares it to the perceptions of users about the disclosure of their information. To this end, a real life dataset is composed. This consists of the Facebook data (images, posts and likes) of 170 people along with their replies to a survey that addresses both their personal information, as well as their perceptions about the sensitivity and the predictability of different types of information. Importantly, we evaluate several learning techniques for the prediction of user attributes based on their OSN data. Our analysis shows that the perceptions of users with respect to the disclosure of specific types of information are often incorrect. For instance, it appears that the predictability of their political beliefs and employment status is higher than they tend to believe. Interestingly, it also appears that information that is characterized by users as more sensitive, is actually more easily predictable than users think, and vice versa (i.e. information that is characterized as relatively less sensitive is less easily predictable than users might have thought).]]>
This paper looks at the problem of privacy in the context of Online Social Networks (OSNs). In particular, it examines the predictability of different types of personal information based on OSN data and compares it to the perceptions of users about the disclosure of their information. To this end, a real life dataset is composed. This consists of the Facebook data (images, posts and likes) of 170 people along with their replies to a survey that addresses both their personal information, as well as their perceptions about the sensitivity and the predictability of different types of information. Importantly, we evaluate several learning techniques for the prediction of user attributes based on their OSN data. Our analysis shows that the perceptions of users with respect to the disclosure of specific types of information are often incorrect. For instance, it appears that the predictability of their political beliefs and employment status is higher than they tend to believe. Interestingly, it also appears that information that is characterized by users as more sensitive, is actually more easily predictable than users think, and vice versa (i.e. information that is characterized as relatively less sensitive is less easily predictable than users might have thought).]]> Wed, 14 Sep 2016 09:39:20 GMT /slideshow/perceived-versus-actual-predictability-of-personal-information-in-social-networks/66010082 EleftheriosSpyromitr@slideshare.net(EleftheriosSpyromitr) Perceived versus Actual Predictability of Personal Information in Social Networks EleftheriosSpyromitr This paper looks at the problem of privacy in the context of Online Social Networks (OSNs). In particular, it examines the predictability of diff�erent types of personal information based on OSN data and compares it to the perceptions of users about the disclosure of their information. To this end, a real life dataset is composed. This consists of the Facebook data (images, posts and likes) of 170 people along with their replies to a survey that addresses both their personal information, as well as their perceptions about the sensitivity and the predictability of diff�erent types of information. Importantly, we evaluate several learning techniques for the prediction of user attributes based on their OSN data. Our analysis shows that the perceptions of users with respect to the disclosure of speci�fic types of information are often incorrect. For instance, it appears that the predictability of their political beliefs and employment status is higher than they tend to believe. Interestingly, it also appears that information that is characterized by users as more sensitive, is actually more easily predictable than users think, and vice versa (i.e. information that is characterized as relatively less sensitive is less easily predictable than users might have thought). <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/perceivedversusactual-160914093920-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> This paper looks at the problem of privacy in the context of Online Social Networks (OSNs). In particular, it examines the predictability of diff�erent types of personal information based on OSN data and compares it to the perceptions of users about the disclosure of their information. To this end, a real life dataset is composed. This consists of the Facebook data (images, posts and likes) of 170 people along with their replies to a survey that addresses both their personal information, as well as their perceptions about the sensitivity and the predictability of diff�erent types of information. Importantly, we evaluate several learning techniques for the prediction of user attributes based on their OSN data. Our analysis shows that the perceptions of users with respect to the disclosure of speci�fic types of information are often incorrect. For instance, it appears that the predictability of their political beliefs and employment status is higher than they tend to believe. Interestingly, it also appears that information that is characterized by users as more sensitive, is actually more easily predictable than users think, and vice versa (i.e. information that is characterized as relatively less sensitive is less easily predictable than users might have thought).

Perceived versus Actual Predictability of Personal Information in Social Networks from Eleftherios Spyromitros-Xioufis

]]> 331 4 https://cdn.slidesharecdn.com/ss_thumbnails/perceivedversusactual-160914093920-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post

http://activitystrea.ms/schema/1.0/posted

0 Personalized Privacy-Aware Image Classification /slideshow/personalized-privacyaware-image-classification/62865335 icmr2016-short-160608180727
Presentation at ICMR 2016]]>
Presentation at ICMR 2016]]> Wed, 08 Jun 2016 18:07:27 GMT /slideshow/personalized-privacyaware-image-classification/62865335 EleftheriosSpyromitr@slideshare.net(EleftheriosSpyromitr) Personalized Privacy-Aware Image Classification EleftheriosSpyromitr Presentation at ICMR 2016 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/icmr2016-short-160608180727-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> Presentation at ICMR 2016

Personalized Privacy-Aware Image Classification from Eleftherios Spyromitros-Xioufis

]]> 348 8 https://cdn.slidesharecdn.com/ss_thumbnails/icmr2016-short-160608180727-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post

http://activitystrea.ms/schema/1.0/posted

0 Kaggle's WISE 2014 challenge /slideshow/my-approach-at-wise-2014-challange/40171288 spyromitroswise2014slides-141012133138-conversion-gate02
This presentation describes the approach that I developed for Kaggle's WISE 2014 challenge. The challenge was about multi-label classification of printed media articles to topics. The main ingredients of my solution was a plug-in rule approach for F1 maximization, feature selection using a chi squared based criterion, feature normalization and a multi-view ensemble scheme.]]>
This presentation describes the approach that I developed for Kaggle's WISE 2014 challenge. The challenge was about multi-label classification of printed media articles to topics. The main ingredients of my solution was a plug-in rule approach for F1 maximization, feature selection using a chi squared based criterion, feature normalization and a multi-view ensemble scheme.]]> Sun, 12 Oct 2014 13:31:38 GMT /slideshow/my-approach-at-wise-2014-challange/40171288 EleftheriosSpyromitr@slideshare.net(EleftheriosSpyromitr) Kaggle's WISE 2014 challenge EleftheriosSpyromitr This presentation describes the approach that I developed for Kaggle's WISE 2014 challenge. The challenge was about multi-label classification of printed media articles to topics. The main ingredients of my solution was a plug-in rule approach for F1 maximization, feature selection using a chi squared based criterion, feature normalization and a multi-view ensemble scheme. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/spyromitroswise2014slides-141012133138-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> This presentation describes the approach that I developed for Kaggle's WISE 2014 challenge. The challenge was about multi-label classification of printed media articles to topics. The main ingredients of my solution was a plug-in rule approach for F1 maximization, feature selection using a chi squared based criterion, feature normalization and a multi-view ensemble scheme.

Kaggle's WISE 2014 challenge from Eleftherios Spyromitros-Xioufis

]]> 1004 3 https://cdn.slidesharecdn.com/ss_thumbnails/spyromitroswise2014slides-141012133138-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds presentation White http://activitystrea.ms/schema/1.0/post

http://activitystrea.ms/schema/1.0/posted

0 Spyromitros ijcai2011slides /slideshow/spyromitros-ijcai2011slides/15728042 spyromitrosijcai2011slides-121221120757-phpapp01
]]>
]]> Fri, 21 Dec 2012 12:07:57 GMT /slideshow/spyromitros-ijcai2011slides/15728042 EleftheriosSpyromitr@slideshare.net(EleftheriosSpyromitr) Spyromitros ijcai2011slides EleftheriosSpyromitr <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/spyromitrosijcai2011slides-121221120757-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /><br>

Spyromitros ijcai2011slides from Eleftherios Spyromitros-Xioufis

]]> 284 2 https://cdn.slidesharecdn.com/ss_thumbnails/spyromitrosijcai2011slides-121221120757-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post

http://activitystrea.ms/schema/1.0/posted

0 https://cdn.slidesharecdn.com/profile-photo-EleftheriosSpyromitr-48x48.jpg?cb=1523586532 I hold a B.Sc. in Informatics, a M.Sc. in Information Systems and a Ph.D. in Machine Learning from the School of Informatics of the Aristotle University of Thessaloniki (AUTH). Since January 2012 I work as a research associate at the Multimedia Knowledge and Social Media Analytics Laboratory of the Information Technologies Institute (CERTH-ITI) where I conduct research on various topics related to Machine Learning and Data Mining. My main research interests are: -multi-target prediction (classification/regression) -image-text annotation/retrieval -learning from data streams -recommender systems users.auth.gr/espyromi/index.html https://cdn.slidesharecdn.com/ss_thumbnails/perceivedversusactual-160914093920-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/perceived-versus-actual-predictability-of-personal-information-in-social-networks/66010082 Perceived versus Actua... https://cdn.slidesharecdn.com/ss_thumbnails/icmr2016-short-160608180727-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/personalized-privacyaware-image-classification/62865335 Personalized Privacy-A... https://cdn.slidesharecdn.com/ss_thumbnails/spyromitroswise2014slides-141012133138-conversion-gate02-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/my-approach-at-wise-2014-challange/40171288 Kaggle's WISE 2014 cha...