• Journal of the Korea Institute of Information Security & Cryptology
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• v.16 no.2
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• pp.13-24
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• 2006

We consider a privacy-preserving cooperative computation protocol evaluating a beneficial function of all participants' secret inputs, such that each party finally holds a share of the function output. We propose a practical privacy-preserving cooperative computation protocol for solving the linear system of equations problem md the linear least-squares problem. Solutions to these problems are widely used in many areas such as banking, manufacturing, and telecommunications. Our multi-party protocol is an efficiently extended version of the previous two-party model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.12
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• pp.5135-5149
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• 2015

Communications among multi-party must be fast, cost effective and secure. Today's computing environments such as internet conference, multi-user games and many more applications involve multi-party. All participants together establish a common session key to enable multi-party and secure exchange of messages. Multi-party password-based authenticated key exchange scheme allows users to communicate securely over an insecure network by using easy-to-remember password. Kwon et al. proposed a practical three-party password-based authenticated key exchange (3-PAKE) scheme to allow two users to establish a session key through a server without pre-sharing a password between users. However, Kwon et al.'s scheme cannot meet the security requirements of key authentication, key confirmation and anonymity. In this paper, we present a novel, simple and efficient multi-party password-based authenticated key exchange (M-PAKE) scheme based on the elliptic curve cryptography for mobile environment. Our proposed scheme only requires two round-messages. Furthermore, the proposed scheme not only satisfies security requirements for PAKE scheme but also achieves efficient computation and communication.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.1
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• pp.431-443
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• 2016

Secure multi-party computation (MPC) has been a research focus of cryptography in resent studies. However, hiding the topology of the network in secure computation is a rather novel goal. Inspired by a seminal paper [1], we proposed a topology-hiding broadcast protocol based on NTRUEncrypt and secret sharing. The topology is concealed as long as any part of the network is corrupted. And we also illustrated the merits of our protocol by performance and security analysis.

• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.1
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• pp.61-66
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• 2019

FinTech has expanded to the extent that not only businesses but almost everyone can feel the impact. The spread of the scope of use has introduced a variety of new financial services that are changing the way we live. In these environments, it is important to develop reliable security measures to protect against cyber attacks. The number of mobile financial transactions in the financial sector is also increasing, making security vulnerable. In this study, we studied security through mutual authentication method that can safely handle financial security and focused on FinTech's security processing through multi-party mutual authentication method that strongly prevents leakage of information even in the event of continuous and sophisticated attacks.

• Journal of Information Processing Systems
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• v.19 no.2
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• pp.240-257
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• 2023

The industrial Internet of Things (IIoT) is characterized by intelligent connection, real-time data processing, collaborative monitoring, and automatic information processing. The heterogeneous IIoT devices require a high data rate, high reliability, high coverage, and low delay, thus posing a significant challenge to information security. High-performance edge and cloud servers are a good backup solution for IIoT devices with limited capabilities. However, privacy leakage and network attack cases may occur in heterogeneous IIoT environments. Cloud-based multi-party computing is a reliable privacy-protecting technology that encourages multiparty participation in joint computing without privacy disclosure. However, the default cloud selection method does not meet the heterogeneous IIoT requirements. The server can be dishonest, significantly increasing the probability of multi-party computation failure or inefficiency. This paper proposes a blockchain and smart contract-based optimized cloud node selection framework. Different participants choose the best server that meets their performance demands, considering the communication delay. Smart contracts provide a progressive request mechanism to increase participation. The simulation results show that our framework improves overall multi-party computing efficiency by up to 44.73%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.3
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• pp.411-427
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• 2010

Data sharing is an essential process for collaborative works particularly in the banking, finance and healthcare industries. These industries require many collaborative works with their internal and external parties such as branches, clients, and service providers. When data are shared among collaborators, security and privacy concerns becoming crucial issues and cannot be avoided. Privacy is an important issue that is frequently discussed during the development of collaborative systems. It is closely related with the security issues because each of them can affect the other. The tradeoff between privacy and security is an interesting topic that we are going to address in this paper. In view of the practical problems in the existing approaches, we propose a collaborative framework which can be used to facilitate concurrent operations, single point failure problem, and overcome constraints for two-party computation. Two secure computation protocols will be discussed to demonstrate our collaborative framework.

• Annual Conference of KIPS
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• 2011.11a
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• pp.945-947
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• 2011

클라우드 컴퓨팅 인프라를 사용할 때 사용자의 민감한 정보가 포함된 데이터를 사용하게 될 수 있다. 데이터를 아웃소싱하여 처리하는 경우 클라우드 제공자가 데이터 처리자로서 사용자의 데이터에 접근해야 한다. 사용자는 데이터를 처리하는 과정에서 행하는 클라우드 제공자의 동작을 알 수 없으므로 클라우드 컴퓨팅을 사용하는 것을 불안해하게 되고 공개를 해도 되는 일부의 데이터만을 사용하게 된다. 본 연구에서는 클라우드 컴퓨팅을 통해 연산을 수행하는 환경에서 사용자의 데이터를 보호하기 위한 연구의 일환으로써, 시스템 및 환경을 정의하고 주로 발생할 수 있는 정보보호 위협을 정리하였다. 또한 현재 연구가 진행되고 있는 SMPC(Secure Multi-Party Computation)을 소개하고 이를 클라우드 컴퓨팅을 통해 연산을 수행하는 환경에 적용하기 위해 고려해야 할 사항들을 제시하며, 향후 연구 방향을 모색한다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.4
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• pp.725-734
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• 2024

The rapidly growing metaverse environment has received widespread attention across various fields such as health and medicine, culture and gaming, as well as politics. However, the excessive collection of personal data by the diverse sensors and devices used in the metaverse environment poses a substantial threat to user privacy. In this paper, we investigate existing cases of secure Multi-Party Computation(MPC) applications, examine the services anticipated to be necessary for the expansion of the metaverse environment, and analyze the privacy issues present in the metaverse environment as well as the limitations of current real-world services. Based on these findings, we propose application scenarios that utilize MPC to preserve user privacy in the metaverse environment. These proposed MPC application scenarios present a new perspective in metaverse security research. In the future, they are expected to be utilized in the development of secure metaverse services.

• Journal of KIISE
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• v.41 no.10
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• pp.799-809
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• 2014

In this paper, we address the problem of computing Pearson correlation coefficients and Spearman's rank correlation coefficients in a secure manner while data providers preserve privacy of their own data in distributed environment. For a data mining or data analysis in the distributed environment, data providers(data owners) need to share their original data with each other. However, the original data may often contain very sensitive information, and thus, data providers do not prefer to disclose their original data for preserving privacy. In this paper, we formally define the secure correlation computation, SCC in short, as the problem of computing correlation coefficients in the distributed computing environment while preserving the data privacy (i.e., not disclosing the sensitive data) of multiple data providers. We then present SCC solutions for Pearson and Spearman's correlation coefficients using secure scalar product. We show the correctness and secure property of the proposed solutions by presenting theorems and proving them formally. We also empirically show that the proposed solutions can be used for practical applications in the performance aspect.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.5
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• pp.833-840
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• 2015

In this paper, we are interested in a generalization of zero-knowledge interactive protocols between prover and verifier, especially to show that the product of an encrypted polynomial and a random polynomial, but published by a secure commitment scheme was correctly computed by the prover. To this end, we provide a generalized protocol for proving that the resulting polynomial is correctly computed by an encrypted polynomial and another committed polynomial. Further we show that the protocol is also secure in the random oracle model. We expect that our generalized protocol can play a role of building blocks in implementing secure multi-party computation including private set operations.