Ubiquitous Social Networks: Opportunities and Challenges for Privacy-Aware User Modelling

Privacy has been recognized as an important topic in the Internet for a long time, and technological developments in the area of privacy tools are ongoing. However, their focus was mainly on the individual. With the proliferation of social network sites, it has become more evident that the problem of privacy is not bounded by the perimeters of individuals but also by the privacy needs of their social networks. The objective of this paper is to contribute to the discussion about privacy in social network sites, a topic which we consider to be severely under-researched. We propose a framework for analyzing privacy requirements and for analyzing privacy-related data. We outline a combination of requirements analysis, conflict-resolution techniques, and a P3P extension that can contribute to privacy within such sites.World Wide Web, privacy, social network analysis, requirements analysis, privacy negotiation, ubiquity, P3P

PRIMA — Privacy research through the perspective of a multidisciplinary mash up

Based on a summary description of privacy protection research within three fields of inquiry, viz. social sciences, legal science, and computer and systems sciences, we discuss multidisciplinary approaches with regard to the difficulties and the risks that they entail as well as their possible advantages. The latter include the identification of relevant perspectives of privacy, increased expressiveness in the formulation of research goals, opportunities for improved research methods, and a boost in the utility of invested research efforts

Group privacy management strategies and challenges in Facebook : a focus group study among Flemish youth organizations

A large body of research has studied young people’s privacy practices and needs in Facebook. Less is known about group privacy. In this study 12 focus groups were organized with a total of 78 adolescents and young adults of local Flemish youth organizations to discuss their privacy practices. Findings describe how different strategies are used to coordinate the group information flow. The study also shows how online group privacy management can be challenging because ‘implicit’ privacy rules need to be made ‘explicit’, personal boundaries may conflict with those of the group one belongs to and privacy turbulence is difficult to define

Privacy-Preserving Trust Management Mechanisms from Private Matching Schemes

Cryptographic primitives are essential for constructing privacy-preserving communication mechanisms. There are situations in which two parties that do not know each other need to exchange sensitive information on the Internet. Trust management mechanisms make use of digital credentials and certificates in order to establish trust among these strangers. We address the problem of choosing which credentials are exchanged. During this process, each party should learn no information about the preferences of the other party other than strictly required for trust establishment. We present a method to reach an agreement on the credentials to be exchanged that preserves the privacy of the parties. Our method is based on secure two-party computation protocols for set intersection. Namely, it is constructed from private matching schemes.Comment: The material in this paper will be presented in part at the 8th DPM International Workshop on Data Privacy Management (DPM 2013

End-to-End Privacy for Open Big Data Markets

The idea of an open data market envisions the creation of a data trading model to facilitate exchange of data between different parties in the Internet of Things (IoT) domain. The data collected by IoT products and solutions are expected to be traded in these markets. Data owners will collect data using IoT products and solutions. Data consumers who are interested will negotiate with the data owners to get access to such data. Data captured by IoT products will allow data consumers to further understand the preferences and behaviours of data owners and to generate additional business value using different techniques ranging from waste reduction to personalized service offerings. In open data markets, data consumers will be able to give back part of the additional value generated to the data owners. However, privacy becomes a significant issue when data that can be used to derive extremely personal information is being traded. This paper discusses why privacy matters in the IoT domain in general and especially in open data markets and surveys existing privacy-preserving strategies and design techniques that can be used to facilitate end to end privacy for open data markets. We also highlight some of the major research challenges that need to be address in order to make the vision of open data markets a reality through ensuring the privacy of stakeholders.Comment: Accepted to be published in IEEE Cloud Computing Magazine: Special Issue Cloud Computing and the La

Managing U.S-EU Trade Relations through Mutual Recognition and Safe Harbor Agreements:"New" and "Global" Approaches to Transatlantic Economic Governance?

governance; regulation; regulations; regulatory competition; directives; implementation; WTO

Cyberpsychology and Human Factors

The online environment has become a significant focus of the everyday behaviour and activities of individuals and organisations in contemporary society. The increasing mediation of communication has led to concerns about the potential risks and associated negative experiences which can occur to users, particularly children and young people. This is related to the emergence of the online environment as a location for criminal and abusive behaviour (e.g., harassment, sexual exploitation, fraud, hacking, malware). One of the key aspects of understanding online victimisation and engagement in criminal behaviours is the characteristics of online communication that are related to the affordances of the technologies, services and applications which constitute digital environments. The aim of this paper is to examine the influence of these characteristics on individual and group behaviour, as well as the associated opportunities for victimisation and criminal behaviour. These issues are of relevance for those involved in the design and implementation of technologies and services, as the ability to assess their potential use in this way can enhance strategies for improving the security of systems and users. It can also inform educational strategies for increasing user understanding of potential informational, privacy and personal risks, and associated steps to improve their security and privacy. Each of the main characteristics of mediated communication is examined, as well as their potential impact on individual and group behaviour, and associated opportunities for victimisation and offending. The article ends by considering the importance of recognising these issues when designing and implementing new technologies, services and applications

BFF: A tool for eliciting tie strength and user communities in social networking services

The final publication is available at Springer via http://dx.doi.org/ 10.1007/s10796-013-9453-6The use of social networking services (SNSs) such as Facebook has explosively grown in the last few years. Users see these SNSs as useful tools to find friends and interact with them. Moreover, SNSs allow their users to share photos, videos, and express their thoughts and feelings. However, users are usually concerned about their privacy when using SNSs. This is because the public image of a subject can be affected by photos or comments posted on a social network. In this way, recent studies demonstrate that users are demanding better mechanisms to protect their privacy. An appropriate approximation to solve this could be a privacy assistant software agent that automatically suggests a privacy policy for any item to be shared on a SNS. The first step for developing such an agent is to be able to elicit meaningful information that can lead to accurate privacy policy predictions. In particular, the information needed is user communities and the strength of users' relationships, which, as suggested by recent empirical evidence, are the most important factors that drive disclosure in SNSs. Given the number of friends that users can have and the number of communities they may be involved on, it is infeasible that users are able to provide this information without the whole eliciting process becoming confusing and time consuming. In this work, we present a tool called Best Friend Forever (BFF) that automatically classifies the friends of a user in communities and assigns a value to the strength of the relationship ties to each one. We also present an experimental evaluation involving 38 subjects that showed that BFF can significantly alleviate the burden of eliciting communities and relationship strength.This work has been partially supported by CONSOLIDER-INGENIO 2010 under grant CSD2007-00022, and TIN 2008-04446 and PROMETEO II/2013/019 projects. 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