A Privacy-Preserving Social P2P Infrastructure for People-Centric Sensing

The rapid miniaturization and integration of sensor technologies into mobile Internet devices combined with Online Social Networks allows for enhanced sensor information querying, subscription, and task placement within People-Centric Sensing networks. However, PCS systems which exploit knowledge about OSN user profiles and context information for enhanced service provision might cause an unsolicited application and dissemination of highly personal and sensitive data. In this paper, we propose a protocol extension to our OSN design Vegas which enables secure, privacy-preserving, and trustful P2P communication between PCS participants. By securing knowledge about social links with standard public key cryptography, we achieve a degree of anonymity at a trust level which is almost good as that provided by a centralized trusted third party

Self-regulatory information sharing in participatory social sensing

Participation in social sensing applications is challenged by privacy threats. Large-scale access to citizens’ data allow surveillance and discriminatory actions that may result in segregation phenomena in society. On the contrary are the benefits of accurate computing analytics required for more informed decision-making, more effective policies and regulation of techno-socio-economic systems supported by ‘Internet-of Things’ technologies. In contrast to earlier work that either focuses on privacy protection or Big Data analytics, this paper proposes a self-regulatory information sharing system that bridges this gap. This is achieved by modeling information sharing as a supply-demand system run by computational markets. On the supply side lie the citizens that make incentivized but self-determined decisions about the level of information they share. On the demand side stand data aggregators that provide rewards to citizens to receive the required data for accurate analytics. The system is empirically evaluated with two real-world datasets from two application domains: (i) Smart Grids and (ii) mobile phone sensing. Experimental results quantify trade-offs between privacy-preservation, accuracy of analytics and costs from the provided rewards under different experimental settings. Findings show a higher privacy-preservation that depends on the number of participating citizens and the type of data summarized. Moreover, analytics with summarization data tolerate high local errors without a significant influence on the global accuracy. In other words, local errors cancel out. Rewards can be optimized to be fair so that citizens with more significant sharing of information receive higher rewards. All these findings motivate a new paradigm of truly decentralized and ethical data analytics.ISSN:2193-112

Sicherheit und Privatsphäre in Online Sozialen Netzwerken

Online Soziale Netzwerke (OSNs) repräsentieren das vorherrschende Medium zur computergestützten Kommunikation und Verbreitung persönlicher, geschäftlicher oder auch wissenschaftlicher Inhalte. Eine Reihe von Vorkommnissen in der jüngsten Vergangenheit hat gezeigt, dass die Bereitstellung privater Informationen in OSNs mit erheblichen Risiken für die Sicherheit und den Schutz der Privatsphäre seiner Nutzer verbunden ist. Gleiches gilt für die Bereiche Wirtschaft und Wissenschaft. Ursächlich dafür ist die zentralisierte Verwaltung der Nutzer und ihrer publizierten Inhalte unter einer singulären administrativen Domäne. Mit Vegas präsentiert der erste Teil dieser Arbeit ein dezentrales OSN, das mit seiner restriktiven Sicherheitsarchitektur diesem Problem begegnet. Oberstes Ziel ist die technische Umsetzung des Rechts auf informationelle Selbstbestimmung. Dazu schränkt Vegas den Zugriff auf den sozialen Graphen und jeglichen Informationsaustausch auf die Nutzer des eigenen Egonetzwerks ein. Neben der Möglichkeit zur Kommunikation und der Bereitstellung persönlicher Informationen erlauben einige OSNs auch das Browsen des sozialen Graphen und die Suche nach Inhalten anderer Nutzer. Um auch in sicheren und die Privatsphäre schützenden OSNs wie Vegas vom akkumulierten Wissen des sozialen Graphen zu profitieren, beschäftigt sich der zweite Teil dieser Arbeit mit der Entwicklung und Analyse intelligenter Priorisierungsstrategien zur Weiterleitung von Suchanfragen innerhalb dezentraler OSNs. Im Kontext von OSNs werden neue Algorithmen und Protokolle zunächst simulativ evaluiert. Die Grundlage bildet in der Regel der Crawling-Datensatz eines OSNs. Offensichtlich ist das Crawling in sicheren und die Privatsphäre schützenden dezentralen OSNs wie Vegas nicht möglich. Um diesem Problem zu begegnen, beschäftigt sich der dritte Teil dieser Arbeit mit der Entwicklung eines generischen Modells zur künstlichen Erzeugung sozialer Interaktionsgraphen. Neben den strukturellen Besonderheiten zentralisierter und dezentraler Systeme wird erstmals auch das Interaktionsverhalten der Nutzer eines OSNs modelliert. Die Eignung des Modells wird auf der Grundlage gecrawlter sozialer Graphen evaluiert.Online Social Networks (OSNs) represent the dominating media for computer-aided communication and the distribution of personal, commercial, and scientific content. Recently a series of incidents has shown that, for its users, the provision of private information in an OSN can create considerable security and privacy risks. The same statement holds for the commercial and the scientific domain. The problem arises from a centralized organization of users and their published contents and its management through a single administrative domain. To overcome this problem, the first part of this thesis introduces Vegas, a decentralized OSN which is based on a highly restrictive security architecture. The major goal of Vegas is to provide a technical implementation of the right for informational self-determination. Therefore Vegas restricts access to the social graph and the exchange of information to users of the own ego-network. In addition to the possibility to communicate and to provide personal data, several OSNs allow for browsing the social graph and for searching content of other users. To benefit from the accumulated knowledge of the social graph in secure and privacy-preserving OSNs like Vegas, the second part of this thesis addresses the development and the analysis of intelligent prioritization strategies for query forwarding in decentralized OSNs. In context of OSNs, the evaluation of new algorithms and protocols takes place through simulation which is based on crawling data of an OSN. Obviously crawling secure and privacy-preserving OSNs like Vegas is not possible. Therefore the third part of this thesis presents a generic model to synthesize social interaction graphs. Besides structural characteristics of centralized and decentralized OSNs, the model also considers the interaction behavior of its users. Its applicability is evaluated on the basis of social graph crawling data