Abstract
We propose a novel mechanism to release an accurate and differentially private estimate of the link set of social networks. Differential privacy is one of the most common notations to quantify the privacy level of information publication. Several methods have been proposed to publish edge set information, among which one of the notable mechanisms is based on stratified sampling. While it is very scalable, social network information can be significantly altered by this technique. In fact, when we use mechanism based on stratified sampling, a totally random network may get published even when the input network is sparse. We aim to overcome this drawback in our work. We provide an efficient two-stage mechanism to control the edge set size and quality independently. To confirm the practical utility of our proposal, we apply it to the maximum matching problem when the edge information is spread between two different bipartite networks. We validate through experiments that the error induced by our framework is at least 20 times smaller than that of the original stratified sampling based mechanism when privacy level is 5. In addition, the computation time of our framework is 3 times shorter than the original method.
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Adhikari, M.B., Suppakitpaisarn, V., Paul, A., Rangan, C.P. (2020). Two-Stage Framework for Accurate and Differentially Private Network Information Publication. 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_22
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DOI: https://doi.org/10.1007/978-3-030-66046-8_22
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