Cooperative Privacy-Preserving Data Collection Protocol Based on Delocalized-Record Chains

This paper aims to advance the field of data anonymization within the context of Internet of Things (IoT), an environment where data collected may contain sensitive information about users. Specifically, we propose a privacy-preserving data publishing alternative that extends the privacy requirement to the data collection phase. Because our proposal offers privacy-preserving conditions in both the data collecting and publishing, it is suitable for scenarios where a central node collects personal data supplied by a set of devices, typically associated with individuals, without these having to assume trust in the collector. In particular, to limit the risk of individuals' re-identification, the probabilistic k-anonymity property is satisfied during the data collection process and the k-anonymity property is satisfied by the data set derived from the anonymization process. To carry out the anonymous sending of personal data during the collection process, we introduce the delocalized-record chain, a new mechanism of anonymous communication aimed at multi-user environments to collaboratively protect information, which by not requiring third-party intermediaries makes it especially suitable for private IoT networks (besides public IoT networks)

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Die vorliegende Diplomarbeit beschäftigt sich mit dem Thema Anonymität und Privatsphäre im Internet und untersucht dies am Nutzungsverhalten von Social Networks im Internet. So-cial Networks stellen seit geraumer Zeit eine massive Gefährdung für die informationelle Pri-vatheit der Internetuser dar, weil durch ihre Nutzung enorme Mengen an persönlichen Daten veröffentlicht werden, was für die Internetuser gravierenden Folgen haben kann. Die Unter-suchung geht den Fragen nach, welche Bedeutung die Begriffe Anonymität und Privatsphäre im Internet und vor allem in Hinblick auf die Nutzung von Social Networks haben, welche Faktoren den freiwilligen Verzicht auf Anonymität beziehungsweise Privatsphäre im Internet beeinflussen und ob Unterschiede betreffend des Umgangs mit Anonymität und Privatsphäre zwischen realer und virtueller Welt existieren. Den theoretischen Hintergrund liefern zum einen die Erkenntnisse aus der Auseinandersetzung mit den beiden zentralen Begriffen und die grundlegenden theoretischen Konzepte zur computervermittelten Kommunikation, zum anderen die bisherigen Erkenntnisse zu Social Networks im Internet. Für die Untersuchung wurde die Methode der Online-Befragung gewählt, als Untersuchungsgegenstand das Social Network Facebook. In der vorliegenden Untersuchung sind die Befragungsergebnisse von 404 Personen berücksichtigt.This thesis deals with anonymity and privacy in the Internet and examines this at the behav-iour of use of social networks in the Internet. Social networks represent for quite some time a substantial endangerment for the informational privacy of the Internet users because by their use enormous quantities of personal data are published, which can have engraving conse-quences for the Internet users. The survey follows the questions, which meaning the terms anonymity and privacy in the Internet and particularly in view to the use of social networks have, which factors the voluntary renouncement of anonymity and/or privacy in the Internet affect and whether concerning differences handling anonymity and privacy between real and virtual world exist. The theoretical backgrounds are on the one hand the knowledge of the discussion of the two central terms and the fundamental theoretical concepts of the com-puter-mediated communications, and one the other hand the present knowledge of social networks in the Internet. The survey is realised with an online questionnaire, the subject of interest is the social network Facebook. In the survey the results of 404 persons are consid-ered

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

User's Privacy in Recommendation Systems Applying Online Social Network Data, A Survey and Taxonomy

Recommender systems have become an integral part of many social networks and extract knowledge from a user's personal and sensitive data both explicitly, with the user's knowledge, and implicitly. This trend has created major privacy concerns as users are mostly unaware of what data and how much data is being used and how securely it is used. In this context, several works have been done to address privacy concerns for usage in online social network data and by recommender systems. This paper surveys the main privacy concerns, measurements and privacy-preserving techniques used in large-scale online social networks and recommender systems. It is based on historical works on security, privacy-preserving, statistical modeling, and datasets to provide an overview of the technical difficulties and problems associated with privacy preserving in online social networks.Comment: 26 pages, IET book chapter on big data recommender system

Network Performance Improvements for Low-Latency Anonymity Networks

While advances to the Internet have enabled users to easily interact and exchange information online, they have also created several opportunities for adversaries to prey on users’ private information. Whether the motivation for data collection is commercial, where service providers sell data for marketers, or political, where a government censors, blocks and tracks its people, or even personal, for cyberstalking purposes, there is no doubt that the consequences of personal information leaks can be severe. Low-latency anonymity networks have thus emerged as a solution to allow people to surf the Internet without the fear of revealing their identities or locations. In order to provide anonymity to users, anonymity networks route users’ traffic through several intermediate relays, which causes unavoidable extra delays. However, although these networks have been originally designed to support interactive applications, due to a variety of design weaknesses, these networks offer anonymity at the expense of further intolerable performance costs, which disincentivize users from adopting these systems. In this thesis, we seek to improve the network performance of low-latency anonymity networks while maintaining the anonymity guarantees they provide to users today. As an experimentation platform, we use Tor, the most widely used privacy-preserving network that empowers people with low-latency anonymous online access. Since its introduction in 2003, Tor has successfully evolved to support hundreds of thousands of users using thousands of volunteer-operated routers run all around the world. Incidents of sudden increases in Tor’s usage, coinciding with global political events, confirm the importance of the Tor network for Internet users today. We identify four key contributors to the performance problems in low-latency anonymity networks, exemplified by Tor, that significantly impact the experience of low-latency application users. We first consider the lack of resources problem due to the resource-constrained routers, and propose multipath routing and traffic splitting to increase throughput and improve load balancing. Second, we explore the poor quality of service problem, which is exacerbated by the existence of bandwidth-consuming greedy applications in the network. We propose online traffic classification as a means of enabling quality of service for every traffic class. Next, we investigate the poor transport design problem and propose a new transport layer design for anonymous communication networks which addresses the drawbacks of previous proposals. Finally, we address the problem of the lack of congestion control by proposing an ATM-style credit-based hop-by-hop flow control algorithm which caps the queue sizes and allows all relays to react to congestion in the network. Our experimental results confirm the significant performance benefits that can be obtained using our privacy-preserving approaches

Emerging privacy challenges and approaches in CAV systems

The growth of Internet-connected devices, Internet-enabled services and Internet of Things systems continues at a rapid pace, and their application to transport systems is heralded as game-changing. Numerous developing CAV (Connected and Autonomous Vehicle) functions, such as traffic planning, optimisation, management, safety-critical and cooperative autonomous driving applications, rely on data from various sources. The efficacy of these functions is highly dependent on the dimensionality, amount and accuracy of the data being shared. It holds, in general, that the greater the amount of data available, the greater the efficacy of the function. However, much of this data is privacy-sensitive, including personal, commercial and research data. Location data and its correlation with identity and temporal data can help infer other personal information, such as home/work locations, age, job, behavioural features, habits, social relationships. This work categorises the emerging privacy challenges and solutions for CAV systems and identifies the knowledge gap for future research, which will minimise and mitigate privacy concerns without hampering the efficacy of the functions

On the anonymity risk of time-varying user profiles.

Websites and applications use personalisation services to profile their users, collect their patterns and activities and eventually use this data to provide tailored suggestions. User preferences and social interactions are therefore aggregated and analysed. Every time a user publishes a new post or creates a link with another entity, either another user, or some online resource, new information is added to the user profile. Exposing private data does not only reveal information about single users’ preferences, increasing their privacy risk, but can expose more about their network that single actors intended. This mechanism is self-evident in social networks where users receive suggestions based on their friends’ activities. We propose an information-theoretic approach to measure the differential update of the anonymity risk of time-varying user profiles. This expresses how privacy is affected when new content is posted and how much third-party services get to know about the users when a new activity is shared. We use actual Facebook data to show how our model can be applied to a real-world scenario.Peer ReviewedPostprint (published version