Online social networks have become an important part of the online activities on the web and one of the most influencing media. Unconstrained by physical spaces, online social networks offer to web users new interesting means to communicate, interact, and socialize. While these networks make frequent data sharing and inter-user communications instantly possible, privacy-related issues are their obvious much discussed immediate consequences. Although the notion of privacy may take different forms, the ultimate challenge is how to prevent privacy invasion when much personal information is available. In this context, we address privacy-related issues by resorting to social network analysis and link mining techniques. We first describe the fundamental of social networks, their common representations, and the main motivations associated with their use. Afterwards, we particularly show how privacy attacks can build on social network analysis and link mining techniques to reveal user-sensitive information. The chapter concludes with a discussion of some open challenges to address in future privacy-related works.

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Privacy in Online Social Networks

Websites that provide content creation and sharing features have become quite popular recently. These sites allow users to categorize and browse content using âtagsâ or free-text keyword topics. Since users contribute and tag social media content across a variety of social web platforms, creating new knowledge from distributed tag data has become a matter of performing various tasks, including publishing, aggregating, integrating, and republishing tag data. However, there are a number of issues in relation to data sharing and interoperability when processing tag data across heterogeneous tagging platforms. In this paper we introduce a semantic tag model that aims to explicitly offer the necessary structure, semantics and relationships between tags. This approach provides an improved opportunity for representing tag data in the form of reusable constructs at a semantic level. We also demonstrate a prototype that consumes and makes use of shared tag metadata across heterogeneous sources.

/pdf/representing-and-sharing-folksonomies-with-semantics-1jp3a1rk00.pdf

Representing and sharing folksonomies with semantics

Recent years have seen a confluence of two major trends -- the increase of mobile devices such as smart phones as the primary access point to networked information and the rise of social media platforms that connect people. Their convergence supports the emergence of a new class of context-aware geosocial networking applications. While existing systems focus mostly on location, our work centers on models for representing and reasoning about a more inclusive and higher-level notion of context, including the user's location and surroundings, the presence of other people and devices, and the inferred activities in which they are engaged. A key element of our work is the use of collaborative information sharing where devices share and integrate knowledge about their context. This introduces the need for privacy and security mechanisms. We present a framework to provide users with appropriate levels of privacy to protect the personal information their mobile devices are collecting, including the inferences that can be drawn from the information. We use Semantic Web technologies to specify high-level, declarative policies that describe user information sharing preferences. We have built a prototype system that aggregates information from a variety of sensors on the phone, online sources, and sources internal to the campus intranet, and infers the dynamic user context. We show how our policy framework can be effectively used to devise better privacy control mechanisms to control information flow between users in such dynamic mobile systems.

/pdf/preserving-privacy-in-context-aware-systems-380dicx71m.pdf

Preserving Privacy in Context-Aware Systems

There is an increasing interest in providing common web users with access to structured knowledge bases such as DBpedia, for example by means of question answering systems. 
All such question answering systems have in common that they have to map a natural language input, be it spoken or written, to a formal representation in order to extract the correct answer from the target knowledge base.
This is also the case for systems which generate natural language text from a given knowledge base.
The main challenge is how to map natural language (spoken or written) to structured data and vice versa.
To this end, question answering systems require knowledge about how the vocabulary elements used in the available datasets are verbalized in natural language, covering different verbalization variants.
Multilinguality of course increases the complexity of this challenge. 
In this thesis we introduce M-ATOLL, a framework for automatically inducing ontology lexica in multiple languages, to find such verbalization variants. 
We have instantiated the system for three languages, English, German and Spanish, by exploiting a set of language-specific dependency patterns for finding lexicalizations in text corpora. Additionally, we extended our framework to extract complex adjective lexicalizations with a machine-learning-based approach. 
M-ATOLL is the first open-source and multilingual approach for the generation of ontology lexica. In this thesis we present grammatical patterns for three different languages, on which the extraction of lexicalization relies.
 We provide an analysis of these patterns as well as a comparison with those proposed by other state-of-the-art systems. Additionally, we present a detailed evaluation comparing the different approaches with different settings on a publicly available goldstandard, and discuss their potential and limitations.

Generation of multilingual ontology lexica with M-ATOLL : a corpus-based approach for the induction of ontology lexica

An enhanced trust prediction strategy for online social networks using probabilistic reputation features

SUMMARY This article introduces the Friend Of A Friend (FOAF) vocabulary specification as an example of a Semantic Web technology. A real world case study is presented in which FOAF is used to solve several specific problems of identity management. The main goal is to provide some basic theory behind the Semantic Web and then attempt to ground that theory in a practical solution.

Mike Graves

Papers

FOAF: Connecting People on the Semantic Web