An Efficient Approach for Privacy Preserving Distributed Clustering in Semi-honest Model Using Elliptic Curve Cryptography

10.6633/IJNS.201505.17(3).12

Sankita J. Patel；Dharmen Punjani；Devesh C. Jinwala

Elliptic curve cryptography ； K-means clustering ； privacy preservation in data mining ； secure multiparty computation

International Journal of Network Security

17卷3期（2015 / 05 / 01）

328 - 339

英文

In this paper, we propose an approach that illustrates the application of Elliptic Curve Cryptography (ECC) in Privacy-preserving distributed K-Means Clustering over horizontally partitioned dataset. We believe that the conventional cryptographic approaches and secret sharing schemes for privacy-preserving distributed K-Means clustering, are not scalable due to the higher computational and communication cost. Elliptic Curve based cryptosystems offer much better key size to security ratio in comparison. Hence, we use ECC based ElGamal scheme in distributed K-Means clustering to preserve privacy. Our approach avoids multiple cipher operations at each site and hence is efficient in terms of computational cost. We also achieve a reduction in the communication cost by allowing parties to communicate in a ring topology. Our experimental results show that our approach is scalable in terms of dataset size and number of parties in a distributed scenario. We carry out comparative analysis of our approach with existing approaches to highlight the effectiveness of our approach.

1. 林佳儒(2017)。塗裝單壁奈米碳管鰭片熱電致冷晶片散熱效率與熱變形之研究。清華大學動力機械工程學系學位論文。2017。1-191。