Method and Application of Homomorphic Subtraction of the Paillier Cryptosystem in Secure Multi-party Computational Geometry

Liu, Meng; Luo, Yaping; Yang, Chi; Xu, Dianguo; Wu, Taoran

doi:10.1007/978-3-030-48513-9_45

Cited by 2 publications

(6 citation statements)

References 26 publications

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“…• The Z 1 = (a 1 , a 2 , ..., a m a ) and Z 1 = (a 1 , a 2 , ..., a m a ) exchanged in step (9) do not have data leakage, because of the addition of their own random numbers. • In step (11), Alice and Bob compute respectively: Then Alice and Bob send Z 1 = (a 1 , a 2 , ..., a m a ) and Z 1 = (a 1 , a 2 , ..., a m a ) to each other.…”

Section: Correctness Analysismentioning

confidence: 99%

“…Secure multi-party computation (MPC), one of three major technical tools of privacy computing, is a collaborative computing method used to compute various types of data. The research problems of MPC mainly include the following aspects: secure computational geometry [10][11][12] , secure data mining 13,14 , secure statistical analysis 15,16 , secure set relationship judgment 17,18 , etc.…”

mentioning

confidence: 99%

“…As an important research branch of MPC, secure computing geometry can solve practical problems in the military 19 , national defense 20 , finance 21,22 , life 23 and other fields, which has important research significance. Liu studied the secure computing distance between two points 11 . Zhang studied MPC of geometric area and volume 24 .…”

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confidence: 99%

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The secure judgment of graphic similarity against malicious adversaries and its applications

Liu

Luo³

et al. 2023

Sci Rep

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With the advent of the era of big data, privacy computing analyzes and calculates data on the premise of protecting data privacy, to achieve data ‘available and invisible’. As an important branch of secure multi-party computation, the geometric problem can solve practical problems in the military, national defense, finance, life, and other fields, and has important research significance. In this paper, we study the similarity problem of geometric graphics. First, this paper proposes the adjacency matrix vector coding method of isomorphic graphics, and use the Paillier variant encryption cryptography to solve the problem of isomorphic graphics confidentiality under the semi-honest model. Using cryptography tools such as elliptic curve cryptosystem, zero-knowledge proof, and cut-choose method, this paper designs a graphic similarity security decision protocol that can resist malicious adversary attacks. The analysis shows that the protocol has high computational efficiency and has wide application value in terrain matching, mechanical parts, biomolecules, face recognition, and other fields.

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Section: Correctness Analysismentioning

confidence: 99%

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confidence: 99%

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The secure judgment of graphic similarity against malicious adversaries and its applications

Liu

Luo³

et al. 2023

Sci Rep

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“…It mainly studies how to make untrusted parties jointly finish a certain kind of privacy computing without any third party. The research problems of MPC mainly include the following aspects: secure comparison of data [1][2][3][4][5], secure computational geometry [6][7][8], secure data mining [9][10][11], secure statistical analysis [12] [13], secure set relationship judgment [14] [15], etc.…”

Section: Introductionmentioning

confidence: 99%

“…As an important research branch of MPC, secure computing geometry can solve practical problems in the military, national defense, finance, life and other fields, which has important research significance. Liu a e-mail: lx2001.lx@163.com studied the secure computing distance between two points [16]. Zhang studied MPC of geometric area and volume [17].…”

Section: Introductionmentioning

confidence: 99%

The secure judgment of graphic similarity against malicious adversaries

Liu

et al. 2022

Preprint

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With the the advent era of big data, the secure computation calculates data on the premise of protecting data privacy, to realize the availability and invisibility of data. Secure multi-party computation, as one of three major technical tools of privacy computing, can still securely carry out data collaborative computation without a trusted third party. As an important branch of secure multi-party computation, the secure computing geometric problem can solve practical problems in the military, national defense, finance, life, and other fields, which has important research significance. In this paper, the graphic similarity problem is studied. Firstly, this paper proposes the adjacency matrix vector coding method of isomorphic graphics and uses the Paillier variant cryptosystem to securely solve the graphic similarity judgment under the semi-honest model. By using an elliptic curve cryptosystem and zero-knowledge proof to solve the possible malicious attacks under the semi-honest model, a graphic similarity judgment protocol under the malicious model is designed. The protocol can resist malicious attacks, has high computational efficiency, and has wide application value.

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Method and Application of Homomorphic Subtraction of the Paillier Cryptosystem in Secure Multi-party Computational Geometry

Cited by 2 publications

References 26 publications

The secure judgment of graphic similarity against malicious adversaries and its applications

The secure judgment of graphic similarity against malicious adversaries and its applications

The secure judgment of graphic similarity against malicious adversaries

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