De-Anonymization of Networks with Communities: When Quantifications Meet Algorithms

Fu, Xinzhe; Hu, Zhongzhao; Zhang, Xu; Fu, Luoyi; Wang, Xinbing

doi:10.1109/glocom.2017.8254107

Cited by 13 publications

(29 citation statements)

References 14 publications

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“…In sensor networks, where the knowledge of statistical dependencies among sensed data is given, the tasks of target tracking [20,21,22], detection [23], parameter estimation [24,2] are the examples, see [4] for a survey. In social networks, where the underlying social phenomenon of interest such as voting models, rumor/opinion propagation [7] evolves over a given social interaction graph, the inference tasks of distributed consensus-based estimation [6], deanonymization of community-structured social network [8] and distributed observability [5] are studied.Message-passing has manifested as an efficient procedure for inference over graphical models that provide the framework of succinct model of the statistical uncertainty of multi-agents. Examples include belief propagation (BP) [9], max-product [12,10] and references therein.…”

Section: Related Workmentioning

confidence: 99%

“…In this paper, out of many possible inference tasks, we consider the maximum a posteriori (MAP) estimation, which is popularly applied in many applications such as data association for a multi-target tracking problem in sensor networks, community-structured social network de-anonymization problem in social networks [8].…”

Section: Cost-efficient Data Graph Learning Algorithmsmentioning

confidence: 99%

“…One well-recognized inference problem is a data association which determines the correct match between measurements of sensors and target tracks by maximum a posteriori (MAP) estimation that is executed in a distributed fashion by exchanging some information messages. Other examples include target tracking, and detection/estimation in sensor networks [1,2,3,4] and de-anonymization, rumor/infection propagation in social networks [5,6,7,8].…”

Section: Introductionmentioning

confidence: 99%

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Learning Data Dependency with Communication Cost

Jang

Song

2018

Proceedings of the Eighteenth ACM International Symposium on Mobile Ad Hoc Networking and Computing

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In this paper, we consider the problem of recovering a graph that represents the statistical data dependency among nodes for a set of data samples generated by nodes, which provides the basic structure to perform an inference task, such as MAP (maximum a posteriori). This problem is referred to as structure learning. When nodes are spatially separated in different locations, running an inference algorithm requires a non-negligible amount of message passing, incurring some communication cost. We inevitably have the trade-off between the accuracy of structure learning and the cost we need to pay to perform a given message-passing based inference task because the learnt edge structures of data dependency and physical connectivity graph are often highly different. In this paper, we formalize this trade-off in an optimization problem which outputs the data dependency graph that jointly considers learning accuracy and message-passing costs. We focus on a distributed MAP as the target inference task due to its popularity, and consider two different implementations, ASYNC-MAP and SYNC-MAP that have different message-passing mechanisms and thus different cost structures. In ASYNC-MAP, we propose a polynomial time learning algorithm that is optimal, motivated by the problem of finding a maximum weight spanning tree. In SYNC-MAP, we first prove that it is NP-hard and propose a greedy heuristic. For both implementations, we then quantify how the probability that the resulting data graphs from those learning algorithms differ from the ideal data graph decays as the number of data samples grows, using the large deviation principle, where the decaying rate is characterized by some topological structures of both original data dependency and physical connectivity graphs as well as the degree of the trade-off, which provides some guideline on how many samples are necessary to obtain a certain learning accuracy. We validate our theoretical findings through extensive simulations, which confirms that it has a good match.

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Section: Related Workmentioning

confidence: 99%

Section: Cost-efficient Data Graph Learning Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Learning Data Dependency with Communication Cost

Jang

Song

2018

Proceedings of the Eighteenth ACM International Symposium on Mobile Ad Hoc Networking and Computing

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“…One of the key results of [19] is that information with low transmissibility spreads more effectively within a small but densely connected social network. In [20], the authors present a comprehensive study of the community-structured social network deanonymization problem. The main focus of this work is on privacy and anonymization challenges.…”

Section: Introductionmentioning

confidence: 99%

“…None of these critical parameters are accounted for in the existing works such as [1], [7], [9], [10], [8], [11], [12], [13], and [17]. Moreover, even though the majority of existing literature such as [1], [7], [9], [10], [8], [12], [13], and [17] focuses on single community, some works such as [14], [15], [11], [19] and [20], do consider multiple communities. However, these multi-community works do not capture the dependence and effect of the centrality of one social community on the other, which is particularly important for cache placement in real-world D2D networks.…”

Section: Introductionmentioning

confidence: 99%

Social Community-Aware Content Placement in Wireless Device-to-Device Communication Networks

Soorki

Saad

Manshaei

et al. 2019

IEEE Trans. on Mobile Comput.

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In this paper, a novel framework for optimizing the caching of popular user content at the level of wireless user equipments (UEs) is proposed. The goal is to improve content offloading over wireless device-to-device (D2D) communication links. In the considered network, users belong to different social communities while their UEs form a single multi-hop D2D network. The proposed framework allows to exploit the multi-community social context of users for improving the local offloading of cached content in a multihop D2D network. To model the collaborative effect of a set of UEs on content offloading, a cooperative game between the UEs is formulated. For this game, it is shown that the Shapley value (SV) of each UE effectively captures the impact of this UE on the overall content offloading process. To capture the presence of multiple social communities that connect the UEs, a hypergraph model is proposed. Two line graphs, an influence-weighted graph, and a connectivity-weighted graph, are developed for analyzing the proposed hypergaph model. Using the developed line graphs along with the SV of the cooperative game, a precise offloading power metric is derived for each UE within a multi-community, multi-hop D2D network. Then, UEs with high offloading power are chosen as the optimal locations for caching the popular content. Simulation results show that, on the average, the proposed cache placement framework achieves 12%, 19%, and 21% improvements in terms of the number of UEs that received offloaded popular content compared to the schemes based on betweenness, degree, and closeness centrality, respectively.

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Social Network De-anonymization with Overlapping Communities: Analysis, Algorithm and Experiments

et al. 2018

IEEE INFOCOM 2018 - IEEE Conference on Computer Communications

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De-Anonymization of Networks with Communities: When Quantifications Meet Algorithms

Cited by 13 publications

References 14 publications

Learning Data Dependency with Communication Cost

Learning Data Dependency with Communication Cost

Social Community-Aware Content Placement in Wireless Device-to-Device Communication Networks

Social Network De-anonymization with Overlapping Communities: Analysis, Algorithm and Experiments

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