Private Multiparty Set Intersection Protocol in Rational Model

• Emura, Keita

• Miyaji, Atsuko

• Rahman, Mohammad Shahriar

抄録

Privacy-preserving set intersection protocol is desirable for many practical applications. Malicious and semihonest adversarial models in cryptographic settings have been considered mostly to design such protocols for privacy-preserving set intersection. In a semi-honest or malicious model an adversary is assumed to follow or arbitrarily deviate from the protocol, respectively. Protocols in semi-honest model can utilize cheaper cryptographic primitives, but that comes with a cost of weaker security. On the other hand, strong security is guaranteed by the malicious model whereby expensive cyptographic primitives are required. However, achieving a desired level of privacy with efficient computation is what we need for practical implementations. In this paper, we address the multiparty private set intersection problem using simple cryptographic primitives, in which each of the N parties learns no elements other than the intersection of their N private datasets. The private set intersection is constructed in game-theoretic model, where instead of being semi-honest or malicious the parties are viewed as rational and are assumed (only) to act in their own self-interest. We consider both single player deviation and coalitions, and show that our protocol satisfies computational strict Nash equilibrium.

identifier:https://dspace.jaist.ac.jp/dspace/handle/10119/11617

収録刊行物

• 2013 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

  2013 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) 431-438, 2013-07

  Institute of Electrical and Electronics Engineers (IEEE)

キーワード

• Set intersection
• Game theory
• Computational Strict Nash equilibrium
• Commutative Encryption

詳細情報

• CRID: 1050855522074599680
• NII論文ID: 120006675373
• Web Site: http://hdl.handle.net/10119/11617
• 本文言語コード: en
• 資料種別: conference paper
• データソース種別:
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  • CiNii Articles