48,825 research outputs found
Privacy Preserving Network Analysis of Distributed Social Networks
Social network analysis as a technique has been applied to
a diverse set of fields, including, organizational behavior,
sociology, economics and biology. However, for sensitive networks such as hate networks,
trust networks and sexual networks, these techniques have been
sparsely used. This is majorly attributed to the unavailability of network
data. Anonymization is the most commonly used technique for performing
privacy preserving network analysis. The process involves the presence of a
trusted third party, who is aware of the complete network, and
releases a sanitized version of it. In this paper, we propose an
alternative, in which, the desired analysis can be performed by the parties who
distributedly hold the network, such that : (a) no central third party is
required; (b) the topology of the underlying network is kept hidden. We
design multiparty protocols for securely performing few of the commonly
studied social network analysis algorithms. The current paper addresses
a secure implementation of the most commonly used network analysis
measures, which include degree distribution, closeness centrality,
PageRank algorithm and K-shell decomposition algorithm. The designed
protocols are proven to be secure in the presence of an arithmetic black-box
extended with operations like comparison and modulo
Assessing Centrality Without Knowing Connections
We consider the privacy-preserving computation of node influence in
distributed social networks, as measured by egocentric betweenness centrality
(EBC). Motivated by modern communication networks spanning multiple providers,
we show for the first time how multiple mutually-distrusting parties can
successfully compute node EBC while revealing only differentially-private
information about their internal network connections. A theoretical utility
analysis upper bounds a primary source of private EBC error---private release
of ego networks---with high probability. Empirical results demonstrate
practical applicability with a low 1.07 relative error achievable at strong
privacy budget on a Facebook graph, and insignificant
performance degradation as the number of network provider parties grows.Comment: Full report of paper appearing in PAKDD202
Distributed Multi-authority Attribute-based Encryption Scheme for Friend Discovery in Mobile Social Networks
AbstractIn recent years, the rapid expansion of the capability of portable devices, cloud servers and cellular network technologies is the wind beneath the wing of mobile social networks. Compared to traditional web-based online social networks, the mobile social networks can assist users to easily discover and make new social interaction with others. A challenging task is to protect the privacy of the users’ profiles and communications. Existing works are mainly based on traditional cryptographic methods, such as homomorphic and group signatures, which are very computationally costly. In this paper, we propose a novel distributed multi-authority attribute-based encryption scheme to efficiently achieve privacy-preserving without additional special signatures. In addition, the proposed scheme can achieve fine-grained and flexible access control. Detailed analysis demonstrates the effectiveness and practicability of our scheme
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
Systematizing Decentralization and Privacy: Lessons from 15 Years of Research and Deployments
Decentralized systems are a subset of distributed systems where multiple
authorities control different components and no authority is fully trusted by
all. This implies that any component in a decentralized system is potentially
adversarial. We revise fifteen years of research on decentralization and
privacy, and provide an overview of key systems, as well as key insights for
designers of future systems. We show that decentralized designs can enhance
privacy, integrity, and availability but also require careful trade-offs in
terms of system complexity, properties provided, and degree of
decentralization. These trade-offs need to be understood and navigated by
designers. We argue that a combination of insights from cryptography,
distributed systems, and mechanism design, aligned with the development of
adequate incentives, are necessary to build scalable and successful
privacy-preserving decentralized systems
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