Analyzing Privacy in Social Networks--An Interdisciplinary Approach

Netter, Michael; Herbst, Sebastian; Pernul, Günther

doi:10.1109/passat/socialcom.2011.22

Cited by 6 publications

(5 citation statements)

References 38 publications

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“…As an example, we consider the tree of Figure 5. For k = 2, there we have that Ξ k (T ) = {v 3 , v 15 13 , v 14 , v 16 , v 17 } (notice that N 2 =l ξ (v 15 ) can be different from this set, but it always has five vertices).…”

Section: The K-metric Antidimension Of Treesmentioning

confidence: 99%

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k -Metric antidimension: A privacy measure for social graphs

Trujillo-Rasúa

Yero

2016

Information Sciences

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The study and analysis of social graphs impacts on a wide range of applications, such as community decision making support and recommender systems. With the boom of online social networks, such analyses are benefiting from a massive collection and publication of social graphs at large scale. Unfortunately, individuals' privacy right might be inadvertently violated when publishing this type of data. In this article, we introduce (k, )-anonymity; a novel privacy measure aimed at evaluating the resistance of social graphs to active attacks. (k, )-anonymity is based on a new problem in Graph Theory, the k-metric antidimension defined as follows.Let G = (V, E) be a simple connected graph and S = {w 1 , · · · , w t } ⊆ V an ordered subset of vertices. The metric representation of a vertex u ∈ V with respect to S is the t-vector r(u|S) w t )), where d G (u, v) represents the length of a shortest u − v path in G. We call S a k-antiresolving set if k is the largest positive integer such that for every vertex v ∈ V − S there exist other k − 1 different vertices v 1 , · · · , v k−1 ∈ V − S with r(v|S) = r(v 1 |S) = · · · = r(v k−1 |S). The k-metric antidimension of G is the minimum cardinality among all the k-antiresolving sets for G.We address the k-metric antidimension problem by proposing a true-biased algorithm with success rate above 80% when considering random graphs of size at most 100. The proposed algorithm is used to determine the privacy guarantees offered by two real-life social graphs with respect to (k, )-anonymity. We also investigate theoretical properties of the k-metric antidimension of graphs. In particular, we focus on paths, cycles, complete bipartite graphs and trees.

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Section: The K-metric Antidimension Of Treesmentioning

confidence: 99%

“…Interested readers could refer to [13,22,29] for further reading on privacy-preserving publication of social graphs.…”

Section: Related Workmentioning

confidence: 99%

k -Metric antidimension: A privacy measure for social graphs

Trujillo-Rasúa

Yero

2016

Information Sciences

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“…Netter et al [12] introduce an interdisciplinary approach to decompose social Web privacy and the impact of different research directions, including legal, technical, and social perspectives. Li et al [13] analyze existing approaches that compromise relation-privacy and offer secure alternatives.…”

Section: Related Workmentioning

confidence: 99%

Game theoretic attack analysis in online social network (OSN) services

White

Park

Kamhoua

et al. 2013

Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

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In the social media era, the ever-increasing utility of Online Social Networks (OSN) services provide a variety of benefits to users, organizations, and service providers. However, OSN services also introduce new threats and privacy issues regarding the data they are dealing with. For instance, in a reliable OSN service, a user should be able to set up his desired level of information sharing and securely manage sensitive data. Currently, few approaches exist that can model OSNs for the purpose, let alone model the effects that attackers can have on these networks. In this work a novel OSN modeling approach is presented to fill the gap. This model is based on an innovative game theoretical approach and it is analyzed both from a theoretical and simulation-oriented view. The game theoretic model is implemented in order to analyze several attack scenarios. As the results show, there are several scenarios where OSN services are very vulnerable and hence more protection mechanisms should be provided in order to secure the data contained across these networks.

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“…In recent years, several research works have appeared that deal with decreasing the impact of these active attacks (see, for instance, [20]). For other related publications on privacy-preserving methods in social networks, see [15,21,23].…”

Section: Introductionmentioning

confidence: 99%