New Incremental Privacy-Preserving Clustering Protocols

Samet, Saeed; Miri, Ali

doi:10.7763/lnse.2013.v1.53

Cited by 3 publications

(2 citation statements)

References 27 publications

(9 reference statements)

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“…From another perspective, multiparty k-means is developed by conforming to such privacypreserving protocols as secure multiparty computation (SMC) (Samet & Miri, 2007;Upmanyu, Namboodiri,Srinathan&Jawahar,2010).GuidedbySMC,multi-sourceddatacanbesharedby severalpartiesandeachpartyindependentlyproduceskclusterssecurely.Meanwhile,alldataare coordinatedinaprivacy-preservingmanner.Doganayetal. ( 2008)studiedtheprivacyofk-means clusteringprotocolsandhighlightedthesituationwheredataissharedwithintwoandmoreparticipants respectively.Miyajimaetal.…”

Section: Introductionmentioning

confidence: 99%

Privacy-Preserving Hybrid K-Means

Gao¹,

Sun²,

Cui³

et al. 2018

International Journal of Data Warehousing and Mining

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This article describes how the most widely used clustering, k-means, is prone to fall into a local optimum. Notably, traditional clustering approaches are directly performed on private data and fail to cope with malicious attacks in massive data mining tasks against attackers' arbitrary background knowledge. It would result in violation of individuals' privacy, as well as leaks through system resources and clustering outputs. To address these issues, the authors propose an efficient privacy-preserving hybrid k-means under Spark. In the first stage, particle swarm optimization is executed in resilient distributed datasets to initiate the selection of clustering centroids in the k-means on Spark. In the second stage, k-means is executed on the condition that a privacy budget is set as ε/2t with Laplace noise added in each round of iterations. Extensive experimentation on public UCI data sets show that on the premise of guaranteeing utility of privacy data and scalability, their approach outperforms the state-of-the-art varieties of k-means by utilizing swarm intelligence and rigorous paradigms of differential privacy.

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Section: Introductionmentioning

confidence: 99%

Privacy-Preserving Hybrid K-Means

Gao¹,

Sun²,

Cui³

et al. 2018

International Journal of Data Warehousing and Mining

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“…Researching on mining algorithms to build incremental versions that also satisfy the secure level in one of our next target. Though there are some incremental privacy preserving for data mining algorithms such incremental k-means [SS13] or Bayesian networks [SMG13], they are not studied systematically. Providing a general methods to achieve incremental privacy preserving data mining algorithms is a promising challenge.…”

Section: Future Workmentioning

confidence: 99%

Speeding up privacy preserving data mining techniques

Tran¹

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This thesis is completed with great support of many people that I have met along the research journey. First of all, I would like to express my deepest gratitude to my supervisor Associate Professor Ng Wee Keong. His valuable advices and patience have helped me overcome the difficulties of research work. Without his sincere support and guidance, I would not able to complete my thesis. Under his supervision, I have learned how to conduct scientific research and write technical papers. It is my great pleasure to thank the examiners whose meticulous comments and constructive suggestions have helped me improve not only technical contents but also organization of the thesis.

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