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Acknowledgement to Reviewers of Electronics in 2019

Maximized Privacy-Preserving Outsourcing on Support Vector Clustering

by 1,2,*, 3, 3,*, 4 and 5
School of Information Engineering, Xuchang University, Xuchang 461000, He’nan, China
Information Technology Research Base of Civil Aviation Administration of China, Civil Aviation University of China, Tianjin 300300, China
School of Computing and Informatics, University of Louisiana at Lafayette, Lafayette, LA 70503, USA
Department of Computer and Information Sciences, College of Science and Technology, Temple University, Philadelphia, PA 19912, USA
The Sate Key Laboratory of Integrated Service Networks, Xidian University, Xi’an 710071, Shanxi, China
Authors to whom correspondence should be addressed.
Electronics 2020, 9(1), 178;
Received: 16 December 2019 / Revised: 11 January 2020 / Accepted: 15 January 2020 / Published: 17 January 2020
(This article belongs to the Section Computer Science & Engineering)
Despite its remarkable capability in handling arbitrary cluster shapes, support vector clustering (SVC) suffers from pricey storage of kernel matrix and costly computations. Outsourcing data or function on demand is intuitively expected, yet it raises a great violation of privacy. We propose maximized privacy-preserving outsourcing on SVC (MPPSVC), which, to the best of our knowledge, is the first all-phase outsourceable solution. For privacy-preserving, we exploit the properties of homomorphic encryption and secure two-party computation. To break through the operation limitation, we propose a reformative SVC with elementary operations (RSVC-EO, the core of MPPSVC), in which a series of designs make selective outsourcing phase possible. In the training phase, we develop a dual coordinate descent solver, which avoids interactions before getting the encrypted coefficient vector. In the labeling phase, we design a fresh convex decomposition cluster labeling, by which no iteration is required by convex decomposition and no sampling checks exist in connectivity analysis. Afterward, we customize secure protocols to match these operations for essential interactions in the encrypted domain. Considering the privacy-preserving property and efficiency in a semi-honest environment, we proved MPPSVC’s robustness against adversarial attacks. Our experimental results confirm that MPPSVC achieves comparable accuracies to RSVC-EO, which outperforms the state-of-the-art variants of SVC. View Full-Text
Keywords: privacy-preserving cluster analysis; support vector clustering; outsourcing; cloud computing; homomorphic encryption privacy-preserving cluster analysis; support vector clustering; outsourcing; cloud computing; homomorphic encryption
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MDPI and ACS Style

Ping, Y.; Hao, B.; Hei, X.; Wu, J.; Wang, B. Maximized Privacy-Preserving Outsourcing on Support Vector Clustering. Electronics 2020, 9, 178.

AMA Style

Ping Y, Hao B, Hei X, Wu J, Wang B. Maximized Privacy-Preserving Outsourcing on Support Vector Clustering. Electronics. 2020; 9(1):178.

Chicago/Turabian Style

Ping, Yuan, Bin Hao, Xiali Hei, Jie Wu, and Baocang Wang. 2020. "Maximized Privacy-Preserving Outsourcing on Support Vector Clustering" Electronics 9, no. 1: 178.

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