Privacy-Preserving and Outsourced Multi-user K-Means Clustering

TY - GEN

T1 - Privacy-Preserving and Outsourced Multi-user K-Means Clustering

AU - Rao, Fang Yu

AU - Samanthula, Bharath K.

AU - Bertino, Elisa

AU - Yi, Xun

AU - Liu, Dongxi

N1 - Publisher Copyright: © 2015 IEEE.

PY - 2016/3/1

Y1 - 2016/3/1

N2 - Many techniques for privacy-preserving data mining (PPDM) have been investigated over the past decade. Such techniques, however, usually incur heavy computational and communication cost on the participating parties and thus entities with limited resources may have to refrain from participating in the PPDM process. To address this issue, one promising solution is to outsource the tasks to the cloud environment. In this paper, we propose a novel and efficient solution to privacy-preserving outsourced distributed clustering (PPODC) for multiple users based on the k-means clustering algorithm. The main novelty of our solution lies in avoiding the secure division operations required in computing cluster centers through efficient transformation techniques. In addition, we discuss two strategies, namely offline computation and pipelined execution that aim to boost the performance of our protocol. We implement our protocol on a cluster of 16 nodes and demonstrate how our two strategies combined with parallelism can significantly improve the performance of our protocol through extensive experiments using a real dataset.

AB - Many techniques for privacy-preserving data mining (PPDM) have been investigated over the past decade. Such techniques, however, usually incur heavy computational and communication cost on the participating parties and thus entities with limited resources may have to refrain from participating in the PPDM process. To address this issue, one promising solution is to outsource the tasks to the cloud environment. In this paper, we propose a novel and efficient solution to privacy-preserving outsourced distributed clustering (PPODC) for multiple users based on the k-means clustering algorithm. The main novelty of our solution lies in avoiding the secure division operations required in computing cluster centers through efficient transformation techniques. In addition, we discuss two strategies, namely offline computation and pipelined execution that aim to boost the performance of our protocol. We implement our protocol on a cluster of 16 nodes and demonstrate how our two strategies combined with parallelism can significantly improve the performance of our protocol through extensive experiments using a real dataset.

KW - Cloud computing

KW - Encrypted data

KW - K-means clustering

KW - Privacy

UR - http://www.scopus.com/inward/record.url?scp=84964812437&partnerID=8YFLogxK

U2 - 10.1109/CIC.2015.20

DO - 10.1109/CIC.2015.20

M3 - Conference contribution

T3 - Proceedings - 2015 IEEE Conference on Collaboration and Internet Computing, CIC 2015

SP - 80

EP - 89

BT - Proceedings - 2015 IEEE Conference on Collaboration and Internet Computing, CIC 2015

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 1st IEEE International Conference on Collaboration and Internet Computing, CIC 2015

Y2 - 28 October 2015 through 30 October 2015

ER -