8 research outputs found
Link Prediction in Multi-modal Social Networks
No full textOnline social networks like Facebook recommend new friends to users based on an explicit social network that users build by adding each other as friends. The majority of earlier work in link prediction infers new interactions between users by mainly focusing on a single network type. However, users also form several implicit social networks through their daily interactions like commenting on people’s posts or rating similarly the same products. Prior work primarily exploited both explicit and implicit social networks to tackle the group/item recommendation
problem that recommends to users groups to join or items to buy. In this paper, we show that auxiliary information from the useritem network fruitfully combines with the friendship network to enhance friend recommendations. We transform the well-known Katz algorithm to utilize a multi-modal network and provide friend recommendations. We experimentally show that the proposed method is more accurate in recommending friends when compared with two single source path-based algorithms using both synthetic and real data sets
Between privacy and security: the factors that drive intentions to use cyber-security applications
No full textInstalling security applications is a common way to protect against malicious apps, phishing emails, and other threats in mobile operating systems. While these applications can provide essential security protections, they also tend to access large amounts of people's sensitive information. Therefore, individuals need to evaluate the trade-off between the security features and the privacy invasion when deciding on which protection mechanisms to use. In this paper, we examine factors affecting the willingness to install mobile security applications by taking into account the invasion levels and security features of cyber-security applications. To this end, we propose a visual language that depicts the coverage of different security features as well as privacy intrusiveness levels. Our user study (n=300) shows that users assessing security applications find their trade-off balance in highly secure apps with a medium level of privacy invasion. The results indicate that a low privacy invasion might signal that the security application provides less security. We discuss these findings in the context of understanding the trade-off between privacy and security
Are you getting sick? Predicting influenza-like symptoms using human mobility behaviors
No full textAbstract Understanding and modeling the mobility of individuals is of paramount importance for public health. In particular, mobility characterization is key to predict the spatial and temporal diffusion of human-transmitted infections. However, the mobility behavior of a person can also reveal relevant information about her/his health conditions. In this paper, we study the impact of people mobility behaviors for predicting the future presence of flu-like and cold symptoms (i.e. fever, sore throat, cough, shortness of breath, headache, muscle pain, malaise, and cold). To this end, we use the mobility traces from mobile phones and the daily self-reported flu-like and cold symptoms of 29 individuals from February 20, 2013 to March 21, 2013. First of all, we demonstrate that daily symptoms of an individual can be predicted by using his/her mobility trace characteristics (e.g. total displacement, radius of gyration, number of unique visited places, etc.). Then, we present and validate models that are able to successfully predict the future presence of symptoms by analyzing the mobility patterns of our individuals. The proposed methodology could have a societal impact opening the way to customized mobile phone applications, which may detect and suggest to the user specific actions in order to prevent disease spreading and minimize the risk of contagion
Understanding human behavior from personal mobile and online data: personal data disclosure, mobility in public-health, recommendations of social links
No full textPersonal data, generated and continuously collected by modern devices and online services, open new perspectives in human behavior understanding. They can characterize human behavior at a very fine and precise resolution, covering a huge variety of daily life, from communication and individual mobility to complex social phenomena and economic behaviors. While from the research point of view the collection of behavioral data has never been so cost-effective and unobtrusive, at the same time, from the applicative point of view, numerous applications supporting users’ needs have leveraged on the massive availability of personal data and the insights produced by human behavior comprehension. Nevertheless, this scenario also raises unprecedented risks affecting users’ privacy.
The high relevance and effectiveness of digital footprints in capturing and describing human behaviors establishes the basis of this work. In this thesis, we use behavioral personal data, collected online or from mobile phones, to understand human behavior with the aim to support users in their everyday lives, investigating aspects that can be turned into personal or societal value in real application scenarios. In our work, we approached our research problems in a comprehensive way by leveraging on real personal data continuously observed in daily-life.
In particular, in this dissertation we investigate three main problems. Firstly, we understand the attitudes of individuals towards personal mobile data disclosure by using both individual characteristics and dynamic behaviors related to communication and mobility, captured from mobile phones. Secondly, we predict the future health status of individuals, in terms of flu-like and cold symptoms, on the basis of a systematic characterization of their mobility behaviors derived from their mobile phones. Finally, we infer the formation of future links in individuals’ social circle on the basis of multiple information sources related to explicit and implicit relationships that users form.
The main insights resulting from our investigation can support a set of use cases, targeting individual and collective applications. Our findings, in terms of behaviors affecting the personal propensity towards data disclosure, can support a new generation of privacy-tools providing personalized feedback to users when tuning their sharing settings. The insights from our following study can provide individuals with personalized feedback about their health status and support decision-makers towards the adoption of preventive measures for public-health. Finally, our findings in terms of new social link predictions can provide users with more accurate recommendations in social networking applications
Κατανόηση της ανθρώπινης συμπεριφοράς από προσωπικά δεδομένα κινητής και διαδικτύου: αποκάλυψη προσωπικών δεδομένων, κινητικότητα στη δημόσια υγεία, συστάσεις κοινωνικών δεσμών
No full textPersonal data, generated and continuously collected by modern devices and online services, open new perspectives in human behavior understanding. They can characterize human behavior at a very fine and precise resolution, covering a huge variety of daily life, from communication and individual mobility to complex social phenomena and economic behaviors. While from the research point of view the collection of behavioral data has never been so cost-effective and unobtrusive, at the same time, from the applicative point of view, numerous applications supporting users’ needs have leveraged on the massive availability of personal data and the insights produced by human behavior comprehension. Nevertheless, this scenario also raises unprecedented risks affecting users’ privacy. The high relevance and effectiveness of digital footprints in capturing and describing human behaviors establishes the basis of this work. In this thesis, we use behavioral personal data, collected online or from mobile phones, to understand human behavior with the aim to support users in their everyday lives, investigating aspects that can be turned into personal or societal value in real application scenarios. In our work, we approached our research problems in a comprehensive way by leveraging on real personal data continuously observed in daily-life. In particular, in this dissertation we investigate three main problems. Firstly, we under- stand the attitudes of individuals towards personal mobile data disclosure by using both individual characteristics and dynamic behaviors related to communication and mobility, captured from mobile phones. Secondly, we predict the future health status of individuals, in terms of flu- like and cold symptoms, on the basis of a systematic characterization of their mobility behaviors derived from their mobile phones. Finally, we infer the formation of future links in individuals’ social circle on the basis of multiple information sources related to explicit and implicit relationships that users form. The main insights resulting from our investigation can support a set of use cases, targeting individual and collective applications. Our findings, in terms of behaviors affecting the personal propensity towards data disclosure, can support a new generation of privacy-tools providing personalized feedback to users when tuning their sharing settings. The insights from our following study can provide individuals with personalized feedback about their health status and support decision-makers towards the adoption of preventive measures for public-health. Finally, our findings in terms of new social link predictions can provide users with more accurate recommendations in social networking applications.Τα προσωπικά δεδομένα, που παράγονται και συλλέγονται συνεχώς από τις σύγχρονες συσκευές και τις διαδικτυακές υπηρεσίες, ανοίγουν νέες προοπτικές στην κατανόηση της ανθρώπινης συμπεριφοράς. Μπορούν να χαρακτηρίσουν την ανθρώπινη συμπεριφορά σε πολύ λεπτή και ακριβή ανάλυση, καλύπτοντας μια τεράστια ποικιλία της καθημερινής ζωής, από την επικοινωνία και την ατομική κινητικότητα έως τα σύνθετα κοινωνικά φαινόμενα και τις οικονομικές συμπεριφορές. Ενώ από ερευνητική άποψη η συλλογή δεδομένων συμπεριφοράς δεν ήταν ποτέ τόσο αποδοτική και διακριτική, ταυτόχρονα, από άποψη εφαρμογών, πολυάριθμες εφαρμογές που υποστηρίζουν τις ανάγκες των χρηστών έχουν αξιοποιήσει τη μαζική διαθεσιμότητα προσωπικών δεδομένων και τις γνώσεις που προκύπτουν από την κατανόηση της ανθρώπινης συμπεριφοράς. Ωστόσο, αυτό το σενάριο εγείρει επίσης πρωτοφανείς κινδύνους που επηρεάζουν την ιδιωτικότητα των χρηστών. H μεγάλη σημασία και αποτελεσματικότητα των ψηφιακών αποτυπωμάτων στην καταγραφή και περιγραφή της ανθρώπινης συμπεριφοράς αποτελεί τη βάση αυτής της εργασίας. Στην παρούσα διατριβή, χρησιμοποιούμε προσωπικά δεδομένα συμπεριφοράς, τα οποία συλλέγονται στο διαδίκτυο ή από κινητά τηλέφωνα, για την κατανόηση της ανθρώπινης συμπεριφοράς με στόχο την υποστήριξη των χρηστών στην καθημερινή τους ζωή, διερευνώντας πτυχές που μπορούν να μετατραπούν σε προσωπική ή κοινωνική αξία σε πραγματικά σενάρια εφαρμογών. Στην εργασία μας, προσεγγίσαμε τα ερευνητικά μας προβλήματα με έναν ολοκληρωμένο τρόπο, αξιοποιώντας πραγματικά προσωπικά δεδομένα που παρατηρούνται συνεχώς στην καθημερινή ζωή. Eιδικότερα, στην παρούσα διατριβή διερευνούμε τρία βασικά προβλήματα. Πρώτον, κατανοούμε τη στάση των ατόμων απέναντι στην αποκάλυψη προσωπικών δεδομένων κινητής τηλεφωνίας, χρησιμοποιώντας τόσο τα ατομικά χαρακτηριστικά όσο και τις δυναμικές συμπεριφορές που σχετίζονται με την επικοινωνία και την κινητικότητα, οι οποίες καταγράφονται από τα κινητά τηλέφωνα. Δεύτερον, προβλέπουμε τη μελλοντική κατάσταση της υγείας των ατόμων, όσον αφορά συμπτώματα γρίπης και κρυολογήματος, βάσει ενός συστηματικού χαρακτηρισμού των συμπεριφορών κινητικότητας που προέρχονται από τα κινητά τους τηλέφωνα. Τέλος, συμπεραίνουμε το σχηματισμό μελλοντικών δεσμών στον κοινωνικό κύκλο των ατόμων με βάση πολλαπλές πηγές πληροφοριών που σχετίζονται με τις ρητές και άρρητες σχέσεις που δημιουργούν οι χρήστες. Οι κύριες γνώσεις που προκύπτουν από τη διερεύνησή μας μπορούν να υποστηρίξουν ένα σύνολο περιπτώσεων χρήσης, με στόχο ατομικές και συλλογικές εφαρμογές. Τα ευρήματά μας, όσον αφορά τις συμπεριφορές που επηρεάζουν την προσωπική τάση για αποκάλυψη δεδομένων, μπορούν να υποστηρίξουν μια νέα γενιά εργαλείων προστασίας της ιδιωτικής ζωής που παρέχουν εξατομικευμένη ανατροφοδότηση στους χρήστες κατά την επιλογή των ρυθμίσεων κοινής χρήσης. Οι γνώσεις από την ακόλουθη μελέτη μας μπορούν να παρέχουν στα άτομα εξατομικευμένη πληροφόρηση σχετικά με την κατάσταση της υγείας τους και να υποστηρίξουν τους υπεύθυνους λήψης αποφάσεων προς την κατεύθυνση της υιοθέτησης προληπτικών μέτρων για τη δημόσια υγεία. Τέλος, τα ευρήματά μας όσον αφορά στις προβλέψεις κοινωνικών συνδέσμων μπορούν να παρέχουν στους χρήστες πιο ακριβείς συστάσεις σε εφαρμογές κοινωνικής δικτύωσης
Anonymous or Not? Understanding the Factors Affecting Personal Mobile Data Disclosure
No full textThe wide adoption of mobile devices and social media platforms have dramatically increased the collection and sharing of personal information. More and more frequently, users are called to make decisions concerning the disclosure of their personal information. In this study, we investigate the factors affecting users’ choices toward the disclosure of their personal data, including not only their demographic and self-reported individual characteristics, but also their social interactions and their mobility patterns inferred from months of mobile phone data activity. We report the findings of a field study conducted with a community of 63 subjects provided with (i) a smart-phone and (ii) a Personal Data Store (PDS) enabling them to control the disclosure of their data. We monitor the sharing behavior of our participants through the PDS and evaluate the contribution of different factors affecting their disclosing choices of location and social interaction data. Our analysis shows that social interaction inferred by mobile phones is an important factor revealing willingness to share, regardless of the data type. In addition, we provide further insights on the individual traits relevant to the prediction of sharing behavior
My Data Store: Toward User Awareness and Control on Personal Data
No full textThe increasing adoption of smartphones and their capability of collecting personal and contextual information have generated a tremendous increment in the production of (personal) data. The availability of such a huge amount of data represents an invaluable opportunity for organizations and individuals to enable new application scenarios. However, it has also significantly increased the public concern on data privacy. In this paper, we present My Data Store, a tool enabling people to control and share their personal data. We tested My Data Store with 63 participants that used it in order to manage their own data collected from mobile phones and through experience sampling applications. Preliminary results are encouraging showing improvement over the users’ awareness of their personal data and the perceived usefulness of the tool
