Efficient Privacy-Preserving Data Mining in Malicious Model

摘要：

In many distributed data mining settings, disclosure of the original data sets is not acceptable due to privacy concerns. To address such concerns, privacypreserving data mining has been an active research area in recent years. While confidentiality is a key issue, scalability is also an important aspect to assess the performance of a privacy-preserving data mining algorithms for practical applications. With this in mind, Kantarcioglu et al. proposed secure dot product and secure set-intersection protocols for privacy-preserving data mining in malicious adversarial model using zero knowledge proofs, since the assumption of semi-honest adversary is unrealistic in some settings. Both the computation and communication complexities are linear with the number of data items in the protocols proposed by Kantarcioglu et al. In this paper, we build efficient and secure dot product and set-intersection protocols in malicious model. In our work, the complexity of computation and communication for proof of knowledge is always constant (independent of the number of data items), while the complexity of computation and communication for the encrypted messages remains the same as in Kantarcioglu et al.s work (linear with the number of data items). Furthermore, we provide the security model in Universal Composability framework.

关键词： Privacy-preserving Data Mining Malicious Model Thresh-old Two-party Computation Efficiency

作者: Keita Emura Atsuko Miyaji Mohammad Shahriar Rahman

作者单位: Center for Highly Dependable Embedded Systems Technology Japan Advanced Institute of Science and Tec School of Information Science Japan Advanced Institute of Science and Technology 1-1 Asahidai Nomi I

会议类型: 国际会议

会议名称: 6th International Conference on Advanced Data Mining and Applications(第六届先进数据挖掘及应用国际会议 ADMA 2010)

会议地点: 重庆

会议语种:英文

页码: 370-382

在线出版日期: 2010-11-19（万方平台首次上网日期，不代表论文的发表时间）