• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.3
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• pp.720-737
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• 2024

Private set intersection cardinality (PSI-CA) is a typical problem in the field of secure multi-party computation, which enables two parties calculate the cardinality of intersection securely without revealing any information about their sets. And it is suitable for private data protection scenarios where only the cardinality of the set intersection needs to be calculated. However, most of the currently available PSI-CA protocols only meet the security under the semi-honest model and can't resist the malicious behaviors of participants. To solve the problems above, by the application of the variant of Elgamal cryptography and Bloom filter, we propose an efficient PSI-CA protocol with high security. We also present two new operations on Bloom filter called IBF and BIBF, which could further enhance the safety of private data. Using zero-knowledge proof to ensure the safety under malicious adversary model. Moreover, in order to minimize the error in the results caused by the false positive problem, we use Garbled Bloom Filter and key-value pair packing creatively and present an improved PSI-CA protocol. Through experimental comparison with several existing representative protocols, our protocol runs with linear time complexity and more excellent characters, which is more suitable for practical application scenarios.

• Journal of Korea Multimedia Society
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• v.7 no.12
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• pp.1708-1718
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• 2004

In this paper, we are concerned with a digital fingerprinting scheme for multi-purchase where a buyer wants to buy more than a digital content. If we apply previous schemes to multi-purchase protocol, the number of execution of registration step and decryption key should be increased in proportion to that of digital contents to be purchased in order to keep unlinkability. More worse, most of fingerprinting schemes in the literature are based on either secure multi-party computation or general zero-knowledge proofs with very high computational complexity. These high complexities complicate materialization of fingerprinting protocol more and more. In this paper, we propose a multi-purchase fingerprinting scheme with lower computational complexity. In the proposed scheme, a buyer executes just one-time registration step regardless of the number of contents to be purchased. The number of decryption key is constant and independent of the number of contents to be purchased. We can also reduce the computational costs of buyers by introducing a concept of proxy-based fingerprinting protocol.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.14 no.3
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• pp.145-153
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• 2004

Digital fingerprinting schemes are cryptographic methods that a seller can identify a traitor who illegally redistributed digital contents by embedding it into buyer's information. Recently, Josep Domingo-Ferrer suggested an anonymous digital fingerprinting scheme based on committed oblivious transfer protocol. It is significant in the sense that it is completely specified from a computation point of view and is thus readily implementable. But this scheme has the serious problem that it cannot provide the security of buyers. In this paper, we first show how to break the existing committed oblivious transfer-based fingerprinting schemes and then suggest secure fingerprinting scheme by introducing oblivious transfer protocol with two-lock cryptosystem based on discrete logarithm. All computations are performed efficiently and the security degree is strengthened in our proposal.

• Journal of the Korea Society of Computer and Information
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• v.28 no.7
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• pp.77-84
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• 2023

This research originated from the need to enhance the security of secure multiparty computation by ensuring that participants involved in multiparty computations provide truthful inputs that have not been manipulated. While malicious participants can be involved, which goes beyond the traditional security models, malicious behaviors through input manipulation often occur in real-world scenarios, leading to privacy infringements or situations where the accuracy of multiparty computation results cannot be guaranteed. Therefore, in this study, we propose a signature scheme applicable to secure multiparty technologies, combining it with secret sharing to strengthen the accuracy of inputs using authentication techniques. We also investigate methods to enhance the efficiency of authentication through the use of batch authentication techniques. To this end, a scheme capable of input certification was designed by applying a commitment scheme and zero-knowledge proof of knowledge to the CL signature scheme, which is a lightweight signature scheme, and batch verification was applied to improve efficiency during authentication.

• Journal of Information Processing Systems
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• v.20 no.4
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• pp.514-523
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• 2024

In the Internet of Medical Things, due to the sensitivity of medical information, data typically need to be retained locally. The training model of heart disease data can predict patients' physical health status effectively, thereby providing reliable disease information. It is crucial to make full use of multiple data sources in the Internet of Medical Things applications to improve model accuracy. As network communication speeds and computational capabilities continue to evolve, parties are storing data locally, and using privacy protection technology to exchange data in the communication process to construct models is receiving increasing attention. This shift toward secure and efficient data collaboration is expected to revolutionize computer modeling in the healthcare field by ensuring accuracy and privacy in the analysis of critical medical information. In this paper, we train and test a multiparty decision tree model for the Internet of Medical Things on a heart disease dataset to address the challenges associated with developing a practical and usable model while ensuring the protection of heart disease data. Experimental results demonstrate that the accuracy of our privacy protection method is as high as 93.24%, representing a difference of only 0.3% compared with a conventional plaintext algorithm.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.6
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• pp.1079-1090
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• 2014

Data mining is a technique to get the useful information that can be utilized for marketing and pattern analysis by processing the data that we have. However, when we use this technique, data provider's personal data can be leaked by accident. To protect these data from leakage, there were several techniques have been studied to preserve privacy. Vertically partitioned data is a state called that the data is separately provided to various number of user. On these vertically partitioned data, there was some methods developed to distinguishing kth element and (k+1) th element by using score. However, in previous method, we can only use on two-party case, so in this paper, we propose the extended technique by using paillier cryptosystem which can use on multi-party case.

• Annual Conference of KIPS
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• 2008.05a
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• pp.1061-1064
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• 2008

SMC로 불리는 안전한 다자간 계산 프로토콜은 이론적으로 완벽한 프라이버시 보호 기능 및 데이터 정확성을 가지고 있지만 현재의 컴퓨팅 환경에서는 구현이 불가능할 정도로 비효율적이다. 매우 효율적이어서 실용화 되어 있는 랜덤화 기법은 상대적으로 낮은 수준의 프라이버시 보호 기능을 지니고 있다. 최근 SMC와 랜덤화 기법을 적절히 혼합한 형태의 프라이버시 보호 기술이 Teng-Du(2007)에 의해서 제안되었다. 본 논문에서 우리는 Teng-Du의 기법을 면밀히 분석하여 새롭게 구현한 연구 결과를 제시한다. SMC 기술로는 Vaidya-Clifton의 스칼라곱 프로토콜을 채택하고, Agrawal-Jayant-Haritsa가 제안한 랜덤대치 기법을 랜덤화 기술로 선택하여 복합적으로 사용한 프라이버시 보호 기법을 제안한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.12
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• pp.4552-4567
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• 2014

A (n,t,n) secret sharing scheme is to share a secret among n group members, where each member also plays a role of a dealer,and any t shares can be used to recover the secret. In this paper, we propose a strong (k,t,n) verifiable multi-secret sharing scheme, where any k out of n participants operate as dealers. The scheme realizes both threshold structure and adversary structure simultaneously, and removes a trusted third party. The secret reconstruction phase is performed using an additive homomorphism for decreasing the storage cost. Meanwhile, the scheme achieves the pre-verification property in the sense that any participant doesn't need to reveal any information about real master shares in the verification phase. We compare our proposal with the previous (n,t,n) secret sharing schemes from the perspectives of what kinds of access structures they achieve, what kinds of functionalities they support and whether heavy storage cost for secret share is required. Then it shows that our scheme takes the following advantages: (a) realizing the adversary structure, (b) allowing any k out of n participants to operate as dealers, (c) small sized secret share. Moreover, our proposed scheme is a favorable candidate to be used in many applications, such as secure multi-party computation and privacy preserving data mining, etc.

• Annual Conference of KIPS
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• 2023.11a
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• pp.115-118
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• 2023

IoT 장치들은 연구, 의료, 금융, 민생 분야 등에 지원하고 있으며 취약한 보안 메커니즘으로 인하여 IoT 네트워크의 개인정보 안전성이 중요해지고 있다. 안전한 다자간 계산은 서로 믿지 않는 참여자라도 데이터 수요자에게 원본 데이터를 누설하지 않는 범위 안에서 다자간 연합 계산 능력을 제공한다. 상업 네트워크나 산업 네트워크에서는 대량의 데이터는 다른 플랫폼들과 통신하기 때문에 기업이나 개인의 개인정보 데이터가 통신 과정에서 도청될 경우 데이터 보유자에게 막대한 경제적이나 잠재적인 손실이 발생한다. 본 논문에서 데이터 통신 과정을 계층별로 정의하여 블록체인에 기반의 안전한 다자간 계산 아키텍처를 제안한다. 제안하는 이키텍처에서 블록체인을 사용함으로써 데이터의 유효성 및 검증 가능성을 보장한다. 인증된 데이터로 안전한 다자간 계산 수행하기 때문에 통신과정의 보안성 및 기밀성도 확보한다. 암호학 및 블록체인 기술의 지속적 발전 및 활성화에 따라 제안하는 아키텍처가 지속적으로 개선할 잠재력이 있다.

• The KIPS Transactions:PartC
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• v.14C no.5
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• pp.439-452
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• 2007

We study on the privacy preserving data mining, PPDM for short, by using randomization. The theoretical PPDM based on the secure multi-party computation techniques is not practical for its computational inefficiency. So we concentrate on a practical PPDM, especially randomization technique. We survey various privacy measures and study on the privacy preserving mining of association rules by using randomization. We propose a new randomization operator, binomial selector, for privacy preserving technique of association rule mining. A binomial selector is a special case of a select-a-size operator by Evfimievski et al.[3]. Moreover we present some simulation results of detecting an appropriate parameter for a binomial selector. The randomization by a so-called cut-and-paste method in [3] is not efficient and has high variances on recovered support values for large item-sets. Our randomization by a binomial selector make up for this defects of cut-and-paste method.