Differentially Private Community Detection in Attributed Social Networks

Tianxi Ji, Changqing Luo, Yifan Guo, Jinlong Ji, Weixian Liao, Pan Li
Proceedings of The Eleventh Asian Conference on Machine Learning, PMLR 101:16-31, 2019.

Abstract

Community detection is an effective approach to unveil social dynamics among individuals in social networks. In the literature, quite a few algorithms have been proposed to conduct community detection by exploiting the topology of social networks and the attributes of social actors. In practice, community detection is usually conducted by third parties like advertisement companies, hospitals, with access to social networks for different purposes, which can easily lead to privacy breaches. In this paper, we investigate community detection in social networks aiming to protect the privacy of both the network topologies and the users’ attributes. In particular, we propose a new scheme called differentially private community detection (DPCD). DPCD detects communities in social networks via a probabilistic generative model, which can be decomposed into subproblems solved by individual users. The private social relationships and attributes of each user are protected by objective perturbation with differential privacy guarantees. Through both theoretical analysis and experimental validation using synthetic and real world social networks, we demonstrate that the proposed DPCD scheme detects social communities under modest privacy budget.

Cite this Paper

BibTeX
@InProceedings{pmlr-v101-ji19a, title = {Differentially Private Community Detection in Attributed Social Networks}, author = {Ji, Tianxi and Luo, Changqing and Guo, Yifan and Ji, Jinlong and Liao, Weixian and Li, Pan}, booktitle = {Proceedings of The Eleventh Asian Conference on Machine Learning}, pages = {16--31}, year = {2019}, editor = {Lee, Wee Sun and Suzuki, Taiji}, volume = {101}, series = {Proceedings of Machine Learning Research}, month = {17--19 Nov}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v101/ji19a/ji19a.pdf}, url = {https://proceedings.mlr.press/v101/ji19a.html}, abstract = {Community detection is an effective approach to unveil social dynamics among individuals in social networks. In the literature, quite a few algorithms have been proposed to conduct community detection by exploiting the topology of social networks and the attributes of social actors. In practice, community detection is usually conducted by third parties like advertisement companies, hospitals, with access to social networks for different purposes, which can easily lead to privacy breaches. In this paper, we investigate community detection in social networks aiming to protect the privacy of both the network topologies and the users’ attributes. In particular, we propose a new scheme called differentially private community detection (DPCD). DPCD detects communities in social networks via a probabilistic generative model, which can be decomposed into subproblems solved by individual users. The private social relationships and attributes of each user are protected by objective perturbation with differential privacy guarantees. Through both theoretical analysis and experimental validation using synthetic and real world social networks, we demonstrate that the proposed DPCD scheme detects social communities under modest privacy budget.} }
Endnote
%0 Conference Paper %T Differentially Private Community Detection in Attributed Social Networks %A Tianxi Ji %A Changqing Luo %A Yifan Guo %A Jinlong Ji %A Weixian Liao %A Pan Li %B Proceedings of The Eleventh Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2019 %E Wee Sun Lee %E Taiji Suzuki %F pmlr-v101-ji19a %I PMLR %P 16--31 %U https://proceedings.mlr.press/v101/ji19a.html %V 101 %X Community detection is an effective approach to unveil social dynamics among individuals in social networks. In the literature, quite a few algorithms have been proposed to conduct community detection by exploiting the topology of social networks and the attributes of social actors. In practice, community detection is usually conducted by third parties like advertisement companies, hospitals, with access to social networks for different purposes, which can easily lead to privacy breaches. In this paper, we investigate community detection in social networks aiming to protect the privacy of both the network topologies and the users’ attributes. In particular, we propose a new scheme called differentially private community detection (DPCD). DPCD detects communities in social networks via a probabilistic generative model, which can be decomposed into subproblems solved by individual users. The private social relationships and attributes of each user are protected by objective perturbation with differential privacy guarantees. Through both theoretical analysis and experimental validation using synthetic and real world social networks, we demonstrate that the proposed DPCD scheme detects social communities under modest privacy budget.
APA
Ji, T., Luo, C., Guo, Y., Ji, J., Liao, W. & Li, P.. (2019). Differentially Private Community Detection in Attributed Social Networks. Proceedings of The Eleventh Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 101:16-31 Available from https://proceedings.mlr.press/v101/ji19a.html.

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