Visible to the public Differential Privacy Information Publishing Algorithm based on Cluster Anonymity

TitleDifferential Privacy Information Publishing Algorithm based on Cluster Anonymity
Publication TypeConference Paper
Year of Publication2020
AuthorsLin, G., Zhao, H., Zhao, L., Gan, X., Yao, Z.
Conference Name2020 International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE)
Date PublishedJune 2020
PublisherIEEE
ISBN Number978-1-7281-6499-1
Keywordsanonymity, anonymous, artificial intelligence, background attack, Big Data, cluster anonymity, clustering, Clustering algorithms, clustering anonymity, complex background knowledge, Complexity theory, composability, data privacy, Differential privacy, differential privacy information publishing algorithm, Human Behavior, information loss, Internet, Internet of Things, internet technology, Metrics, pattern clustering, privacy budget, pubcrawl, Publishing, resilience, Resiliency, Running efficiency
Abstract

With the development of Internet technology, the attacker gets more and more complex background knowledge, which makes the anonymous model susceptible to background attack. Although the differential privacy model can resist the background attack, it reduces the versatility of the data. In this paper, this paper proposes a differential privacy information publishing algorithm based on clustering anonymity. The algorithm uses the cluster anonymous algorithm based on KD tree to cluster the original data sets and gets anonymous tables by anonymous operation. Finally, the algorithm adds noise to the anonymous table to satisfy the definition of differential privacy. The algorithm is compared with the DCMDP (Density-Based Clustering Mechanism with Differential Privacy, DCMDP) algorithm under different privacy budgets. The experiments show that as the privacy budget increases, the algorithm reduces the information loss by about 80% of the published data.

URLhttps://ieeexplore.ieee.org/document/9196412
DOI10.1109/ICBAIE49996.2020.00054
Citation Keylin_differential_2020