Initial Public Offering (IPO) on Permissioned Blockchain Using Secure Multiparty Computation

Halevi, Tzipora; Benhamouda, Fabrice; De, Angelo; Halevi, Shai; Jutla, Charanjit S.; Manevich, Yacov; Zhang, Qi

doi:10.1109/blockchain.2019.00021

Cited by 17 publications

(23 citation statements)

References 10 publications

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“…In this case, by applying the subgradient method to Eq. (17), the updating of multipliers follows (19). According to the strong duality principle, the primal and dual problems are strictly equivalent.…”

Section: ) the Smpc Framework For Maed Using Dandc Methodsmentioning

confidence: 99%

“…SMPC has been applied to the fields of secure voting [17], data sharing in the medical industry [18], blockchains [19], and data mining [20]. However, the considerable computational costs of conducting sophisticated assignments using SMPC protocols prevents further applications of SMPC.…”

Section: A Indices/setsmentioning

confidence: 99%

“…11 The structure and partition of subgrids in the IEEE 118 bus test system Second, compared to traditional method M1, SMPC-based M2 shows a minor increase in solution time from 3.125 s to 3.375 s. For such a large system with many constraints and decision variables, the implementation of the SMPC-based distributed optimization method maintains a limited and acceptable increase in computational costs. This is because the assignment that needs to be calculated through SMPC protocols during each iteration is limited to several steps of the dot product, which is given in (19). Therefore, the SMPC framework for solving the MAED problem is scalable.…”

Section: ) Large Casementioning

confidence: 99%

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Fully Privacy-Preserving Distributed Optimization Based on Secret Sharing

Tian¹,

Guo²,

Sun³

et al. 2021

Preprint

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With the increasing development of smart grid, multiparty cooperative computation between several entities has become a typical characteristic of modern energy systems. Traditionally, data exchange among parties is inevitable, rendering how to complete multiparty collaborative optimization without exposing any private information a critical issue. This paper proposes a fully privacy-preserving distributed optimization framework based on secure multiparty computation (SMPC) secret sharing protocols. The framework decomposes the collaborative optimization problem into a master problem and several subproblems. The process of solving the master problem is executed in the SMPC framework via the secret sharing protocols among agents. The relationships of agents are completely equal, and there is no privileged agent or any third party. The process of solving subproblems is conducted by agents individually. Compared to the traditional distributed optimization framework, the proposed SMPC-based framework can fully preserve individual private information. Exchanged data among agents are encrypted and no private information disclosure is assured. Furthermore, the framework maintains a limited and acceptable increase in computational costs while guaranteeing optimality. Case studies are conducted to demonstrate the principle of secret sharing and verify the feasibility and scalability of the proposed methodology. <br>

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Section: ) the Smpc Framework For Maed Using Dandc Methodsmentioning

confidence: 99%

Section: A Indices/setsmentioning

confidence: 99%

Section: ) Large Casementioning

confidence: 99%

See 1 more Smart Citation

Fully Privacy-Preserving Distributed Optimization Based on Secret Sharing

Tian¹,

Guo²,

Sun³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…In particular, the work of Benhamouda et al [3] has demonstrated the feasibility of coupling blockchain and MPC. They continued in the same direction with [4] by implementing an Initial Public Offering protocol over Fabric. The IPO protocol is also computed by an MPC protocol to ensure the confidentiality of the data input.…”

Section: Introductionmentioning

confidence: 99%

“…This paper is structured as follow. After an introduction of the necessary preliminaries, we discuss the related work and focus on the work by Benhamouda et al [3], [4]. Then, we present our architecture that we illustrate with an IPO protocol.…”

Section: Introductionmentioning

confidence: 99%

Privacy-Preserving Initial Public Offering using SCALE-MAMBA and Hyperledger Fabric

Benmouffok

Singh

Augot

2021

2021 IEEE 30th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE)

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We consider Initial Public Offering (IPO) on blockchains while preserving privacy using Secure Multiparty Computation (MPC), which allows participants to perform a computation on secret data. We provide "MPC as a service", where users requiring a computation distributes shares of their data to MPC workers who run an MPC protocol on the shares and return the result. Previous work by Benhamouda et al. considered IPO over Hyperledger Fabric. We improve by providing a tighter and easier integration of MPC protocol in Fabric using the MPC library SCALE-MAMBA. We explain the obtained security benefits and experimental results are provided.

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A Blockchain-Based Autonomous System for Primary Financial Market

Liu

Chen

2022

Service-Oriented Computing – ICSOC 2021 Workshops

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Initial Public Offering (IPO) on Permissioned Blockchain Using Secure Multiparty Computation

Cited by 17 publications

References 10 publications

Fully Privacy-Preserving Distributed Optimization Based on Secret Sharing

Fully Privacy-Preserving Distributed Optimization Based on Secret Sharing

Privacy-Preserving Initial Public Offering using SCALE-MAMBA and Hyperledger Fabric

A Blockchain-Based Autonomous System for Primary Financial Market

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