Abstract
Network embedding aims to learn the low-dimensional latent representations of vertices in a network. The existing works have primarily focused on various embedding methods for network data in general and overlooked the privacy security issue of them. For example, when a vertex is deleted from the network, it is easy to achieve the deleted relations by remaining embedding vectors. To address these issues, we propose choosing the node degree with selectivity to study the problem of privacy attack and defense in network embedding. While some existing works are addressed the data protection problem, none of them has paid special attention to combine network embedding with privacy security in such a deep way. Our solution consists of two components. First, we propose a new method named SANE, short for Sampling Attack in Network Embedding to utilize remaining vertex information to obtain the deleted relations between vertices as privacy attack. Second, we propose a new privacy defense algorithm named DPNE, short for Differential Privacy in Network Embedding, to employ obfuscation function to defend against attacks to prevent deleted related relations being recovered. The two components are integrated in a principled way for considering the node degree to classify the vertices into different levels for sampling. We conduct extensive experiments on several real-world datasets and one synthetic dataset covering the task of link prediction. Both quantitative results and qualitative analysis verify the effectiveness and rationality of our methods.
C. Kong and B. Chen—The two authors contributed equally to this work.
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Acknowledgment
This work was supported by the National Natural Science Foundation of China Youth Fund under Grant No. 61902001, the Initial Scientific Research Fund of Introduced Talents in Anhui Polytechnic University under Grant No. 2017YQQ015, the Major Project of Natural Science Research in Colleges and Universities of Anhui Province under Grant No. KJ2019ZD15, and the Natural Science Project of Anhui Education Department under Grant No. KJ2019A0158.
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Kong, C. et al. (2020). Privacy Attack and Defense in Network Embedding. In: Chellappan, S., Choo, KK.R., Phan, N. (eds) Computational Data and Social Networks. CSoNet 2020. Lecture Notes in Computer Science(), vol 12575. Springer, Cham. https://doi.org/10.1007/978-3-030-66046-8_19
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