Privacy-Preserving Data Mining in Presence of Covert Adversaries

摘要：

Disclosure of the original data sets is not acceptable due to privacy concerns in many distributed data mining settings. To address such concerns, privacy-preserving data mining has been an active research area in recent years. All the recent works on privacy-preserving data mining have considered either semi-honest or malicious adversarial models, whereby an adversary is assumed to follow or arbitrarily deviate from the protocol, respectively. While semi-honest model provides weak security requiring small amount of computation and malicious model provides strong security requiring expensive computations like Non-Interactive Zero Knowledge proofs, we envisage the need for covert adversarial model that performs in between the semihonest and malicious models, both in terms of security guarantee and computational cost. In this paper, for the first time in data-mining area, we build efficient and secure dot product and setintersection protocols in covert adversarial model. We use homomorphic property of Paillier encryption scheme and two-party computation of Aumann et al. to construct our protocols. Furthermore, our protocols are secure in Universal Compos-ability framework.

关键词： Privacy-preserving Data Mining Covert Adversary Effi-ciency Multi Party Computation

作者: Atsuko Miyaji Mohammad Shahriar Rahman

作者单位: School of Information Science Japan Advanced Institute of Science and Technology 1-1 Asahidai Nomi Ishikawa Japan 923-1292

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: 429-440

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）