The right to privacy in a Big Data society. Merits and limits of the GDPR

With the non-stop development of technology, Big Data generation has seen a rise like no other. The rise of Big Data has given a possibility to numerous ways in which personal data of consumers could be used leaving the people vulnerable. The European Union came up with GDPR as the latest way of protecting the rights of citizens. In this paper, we analyze different aspects of Big Data such as legal framework, consent, and anonymization and see in what ways GDPR has benefitted in protecting personal data and what its limitations are

Hierarchical categorisation of web tags for Delicious

In the scenario of social bookmarking, a user browsing the Web bookmarks web pages and assigns free-text labels (i.e., tags) to them according to their personal preferences. The benefits of social tagging are clear – tags enhance Web content browsing and search. However, since these tags may be publicly available to any Internet user, a privacy attacker may collect this information and extract an accurate snapshot of users’ interests or user profiles, containing sensitive information, such as health-related information, political preferences, salary or religion. In order to hinder attackers in their efforts to profile users, this report focuses on the practical aspects of capturing user interests from their tagging activity. More accurately, we study how to categorise a collection of tags posted by users in one of the most popular bookmarking services, Delicious (http://delicious.com).Preprin

Graphs behind data: A network-based approach to model different scenarios

openAl giorno d’oggi, i contesti che possono beneficiare di tecniche di estrazione della conoscenza a partire dai dati grezzi sono aumentati drasticamente. Di conseguenza, la definizione di modelli capaci di rappresentare e gestire dati altamente eterogenei è un argomento di ricerca molto dibattuto in letteratura. In questa tesi, proponiamo una soluzione per affrontare tale problema. In particolare, riteniamo che la teoria dei grafi, e più nello specifico le reti complesse, insieme ai suoi concetti ed approcci, possano rappresentare una valida soluzione. Infatti, noi crediamo che le reti complesse possano costituire un modello unico ed unificante per rappresentare e gestire dati altamente eterogenei. Sulla base di questa premessa, mostriamo come gli stessi concetti ed approcci abbiano la potenzialità di affrontare con successo molti problemi aperti in diversi contesti. ​Nowadays, the amount and variety of scenarios that can benefit from techniques for extracting and managing knowledge from raw data have dramatically increased. As a result, the search for models capable of ensuring the representation and management of highly heterogeneous data is a hot topic in the data science literature. In this thesis, we aim to propose a solution to address this issue. In particular, we believe that graphs, and more specifically complex networks, as well as the concepts and approaches associated with them, can represent a solution to the problem mentioned above. In fact, we believe that they can be a unique and unifying model to uniformly represent and handle extremely heterogeneous data. Based on this premise, we show how the same concepts and/or approach has the potential to address different open issues in different contexts. ​INGEGNERIA DELL'INFORMAZIONEopenVirgili, Luc

Personal Learning with Social Media:Reputation, Privacy and Identity Perspectives

Social media platforms are increasingly used in recent years to support learning activities, especially for the construction of activity- and learner-centric personal learning environments (PLEs). This thesis investigates the solutions to four essential design requirements for social media based PLEs: support for help seeking, privacy protection, identity management and activity monitoring, as well as context awareness. Three main components of the thesis, reputation, privacy, and identity, are built upon these four design requirements. We investigate the three components through the following research questions. How do we help learners to find suitable experts or peers who they can learn from or collaborate with in a particular learning context? How can we design a proper privacy mechanism to make sure the information shared by learners is only disclosed to the intended audience in a given context? What identity scheme should be used to preserve the privacy of learners while also providing personalized learning experience, especially for teenage learners? To tackle the design requirement of support for help seeking, we address the reputation dimension in the context of personal learning for doctoral studies, where doctoral students need to find influential experts or peers in a particular domain. We propose an approach to detect a domain-specific community in academic social media platforms. Based on that, we investigate the influence of scholars taking both their academic and social impact into account. We propose a measure called R-Index that aggregates the readership of a scholar's publications to assess her academic impact. Furthermore, we add the social dimension into the influence measure by adopting network centrality metrics in a domain-specific community. Our results show that academic influence and social influence measures do not strongly correlate with each other, which implies that, adding the social dimension could enhance the traditional impact metrics that only take academic influence into account. Moreover, we tackle the privacy dimension of designing a PLE in the context of higher education. To protect against unauthorized access to learners' data, we propose a privacy control approach that allows learners to specify the audience, action, and artifact for their sharing behavior. Then we introduce the notion of privacy protocol with which learners can define fine-grained sharing rules. To provide a usable application of the privacy protocol in social media based PLEs, we exploit the space concept that provides an easy way for users to define the privacy protocols within a particular context. The proposed approach is evaluated through two user studies. The results reveal that learners confirm the usefulness and usability of the privacy enhanced sharing scheme based on spaces. In the last part of the thesis, we study the identity dimension in the context of STEM education at secondary and high schools. To support personalization while also preserving learners' privacy, we propose a classroom-like pseudonymity scheme that allows tracking of learners' activities while keeping their real identities undisclosed. In addition, we present a data storage mechanism called Vault that allows apps to store and exchange data within the scope of a Web-based inquiry learning space

Prediction, evolution and privacy in social and affiliation networks

In the last few years, there has been a growing interest in studying online social and affiliation networks, leading to a new category of inference problems that consider the actor characteristics and their social environments. These problems have a variety of applications, from creating more effective marketing campaigns to designing better personalized services. Predictive statistical models allow learning hidden information automatically in these networks but also bring many privacy concerns. Three of the main challenges that I address in my thesis are understanding 1) how the complex observed and unobserved relationships among actors can help in building better behavior models, and in designing more accurate predictive algorithms, 2) what are the processes that drive the network growth and link formation, and 3) what are the implications of predictive algorithms to the privacy of users who share content online. The majority of previous work in prediction, evolution and privacy in online social networks has concentrated on the single-mode networks which form around user-user links, such as friendship and email communication. However, single-mode networks often co-exist with two-mode affiliation networks in which users are linked to other entities, such as social groups, online content and events. We study the interplay between these two types of networks and show that analyzing these higher-order interactions can reveal dependencies that are difficult to extract from the pair-wise interactions alone. In particular, we present our contributions to the challenging problems of collective classification, link prediction, network evolution, anonymization and preserving privacy in social and affiliation networks. We evaluate our models on real-world data sets from well-known online social networks, such as Flickr, Facebook, Dogster and LiveJournal

Securing Critical Infrastructures

1noL'abstract è presente nell'allegato / the abstract is in the attachmentopen677. INGEGNERIA INFORMATInoopenCarelli, Albert