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このアイテムの引用には次の識別子を使用してください:
http://hdl.handle.net/10119/9592
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| タイトル: | Privacy-Preserving Data Mining in Presence of Covert Adversaries |
| 著者: | Miyaji, Atsuko
Rahman, Mohammad Shahriar |
| キーワード: | Privacy-preserving Data Mining
Covert Adversary
Efficiency
Multi Party Computation |
| 発行日: | 2010 |
| 出版者: | Springer |
| 誌名: | Lecture Notes in Computer Science |
| 巻: | 6440/2010 |
| 開始ページ: | 429 |
| 終了ページ: | 440 |
| DOI: | 10.1007/978-3-642-17316-5_41 |
| 抄録: | 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 semi-honest 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 set-intersection 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 Composability framework. |
| Rights: | This is the author-created version of Springer, Atsuko Miyaji and Mohammad Shahriar Rahman, Lecture Notes in Computer Science, 6440/2010, 2010, 429-440. The original publication is available at www.springerlink.com, http://dx.doi.org/10.1007/978-3-642-17316-5_41 |
| URI: | http://hdl.handle.net/10119/9592 |
| 資料タイプ: | author |
| 出現コレクション: | b10-1. 雑誌掲載論文 (Journal Articles)
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このアイテムのファイル:
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記述 |
サイズ | 形式 |
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| 15721-1.pdf | | 262Kb | Adobe PDF | 見る/開く |
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