• Annual Conference of KIPS
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• 2021.11a
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• pp.189-192
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• 2021

5G와 사물인터넷(IoT) 시대에 데이터의 크로스컴퓨팅은 연구, 의료, 금융, 민생 분야 등에 더 많은 지원을 할 수 있고 프라이버시 안전성이 중요해지고 있다. SMPC (Secure Multi-party Computation)은 서로 믿지 않는 참여자 간의 프라이버시 보호 시너지 컴퓨팅 문제를 해결하고, 데이터 수요자에게 원본 데이터를 누설하지 않는 범위 하에서의 다자간 컴퓨팅 능력을 제공한다. IoT 장치는 전력 소모와 지연에 제한을 받기 때문에 대부분의 장치가 여전히 경량화 보안 메커니즘에 속하고 IoT에서 트래픽의 데이터 통합관리가 어렵기 때문에 통신 중 신원인식과 데이터를 주고받는 단계에서 프라이버시 유출의 문제가 발생할 수 있고 심지어 DDOS공격, RelayAttack공격 등 사이버의 목적이 될 수도 있다. 본 논문에서 IoT 네트워크 데이터 통신 특징을 분석하고 동형 암호에 기반의 SMPC 연산 아키텍처를 제안한다. 제안하는 이키텍처에서 동형 암호를 사용함으로써 장치 데이터의 안전을 보장하는 동시에 전체 네트워크 안전성도 확보한다. SMPC 및 동형암호 기술의 지속적 발전에 따라 제안하는 아키텍처가 계속 개선할 잠재력이 있다.

• 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.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.

• 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.

• Journal of KIISE:Computer Systems and Theory
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• v.34 no.10
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• pp.502-510
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• 2007

Private Matching is a problem of computing the intersection of private datasets of two parties. One could envision the usage of private matching for Insurance fraud detection system, Do-not-fly list, medical databases, and many other applications. In 2004, Freedman et at. [1] introduced a probabilistic solution for this problem, and they extended it to malicious adversary model and multi-party computation. In this paper, we propose a new deterministic protocol for private matching with perfect correctness. We apply this technique to adversary models, achieving more reliable and higher speed computation.

• 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.

• Proceedings of the Korea Multimedia Society Conference
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• 2012.05a
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• pp.349-350
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• 2012

본 논문에서는 다자간 영상통화의 오디오 게인콘트롤을 위한 저연산 음성분류방식을 제안한다. 제안된 음성분류방식은 입력되는 음성신호를 음성신호의 특징에 따라서 묵음/무성음/유성음으로 분류한다. 입력된 음성신호의 에너지를 이용해서 음성구간과 비음성구간을 판별한다. 음성구간으로 판별된 구간에 대해서 ZCR(Zeor Crossing Rate)를 이용하여 유성음과 무성음으로 분류한다. 제안된 방식의 성능을 측정을 위해 음성분류 정확도와 연산시간을 측정하여 성능을 측정하였다.

• 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.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.6
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• pp.1401-1414
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• 2018

The k-means clustering algorithm groups input data with the number of groups represented by variable k. In fact, this algorithm is particularly useful in market segmentation and medical research, suggesting its wide applicability. In this paper, we propose a privacy-preserving clustering algorithm that is appropriate for outsourced encrypted data, while exposing no information about the input data itself. Notably, our proposed model facilitates encryption of all data, which is a large advantage over existing privacy-preserving clustering algorithms which rely on multi-party computation over plaintext data stored on several servers. Our approach compares homomorphically encrypted ciphertexts to measure the distance between input data. Finally, we theoretically prove that our scheme guarantees the security of input data during computation, and also evaluate our communication and computation complexity in detail.