• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.10a
• /
• pp.47-49
• /
• 2021

According to the revision of the Data 3 Act in 2020, personal information of medical data can be processed anonymously for statistical purposes, research, and public interest record keeping. However, unidentified data can be re-identified using genetic information, credit information, etc., and personal health information can be abused as sensitive information. In this paper, we derive the need for homomorphic encryption to protect the privacy of personal information separated by sensitive information.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2021.11a
• /
• pp.189-192
• /
• 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.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.33 no.6
• /
• pp.861-868
• /
• 2023

One of the key technologies in providing data analysis in the deep learning while maintaining security is fully homomorphic encryption. Due to constraints in operations on fully homomorphically encrypted data, non-arithmetic functions used in deep learning must be approximated by polynomials. Until now, the degrees of approximation polynomials with composite minimax polynomials have been uniformly set across layers, which poses challenges for effective network designs on fully homomorphic encryption. This study theoretically proves that setting different degrees of approximation polynomials constructed by composite minimax polynomial in each layer does not pose any issues in the inference on convolutional neural networks.

• The Journal of the Korea Contents Association
• /
• v.21 no.9
• /
• pp.42-50
• /
• 2021

As the Internet environment develops, data-analysis-based applications have been widely and extensively used in the past decade. However, these applications potentially have a privacy problem in that users' personal information may be leaked to unauthorized parties. To tackle such a problem, researchers have suggested several techniques including data perturbation and cryptography. The homomorphic encryption algorithm is a relatively new cryptography technology that allows arithmetic operations for encrypted values as it is without decryption. Since original values are not required, we believe that this method provides better privacy protection than other existing solutions. In this work, we propose to apply a homomorphic encryption algorithm that protects personal information while enabling data analysis.

• Review of KIISC
• /
• v.33 no.4
• /
• pp.103-109
• /
• 2023

암호기술 국제표준화는 각국의 국가표준기구들이 가입된 ISO/IEC의 JTC 1 산하 SC 27 내 WG(Working Group) 2에서 진행되고 있다. 현재 70여 편의 암호기술 표준이 제정되어 있으며, 최근에는 양자컴퓨터 위협에 대응하기 위한 양자내성 공개키암호와 전자서명, 데이터 보안에 활용할 수 있는 완전동형암호 및 다자간 안전계산의 표준화가 주를 이루고 있다, 본 고에서는 전체적인 표준화 현황을 간략하게 살펴보고, 최신 이슈가 되고 있는 표준화 현황에 관해 설명한다.

• Journal of the Korea Society for Simulation
• /
• v.24 no.4
• /
• pp.153-161
• /
• 2015

We propose a privacy-preserving location-based service (LBS) which supports top-k search service. The previous schemes hurt the privacy of either the user and the location of the objects because they are sent to the LBS server in a plaintext form. In the proposed method, by encrypting them with the fully-homomorphic encryption, we achieved the top-k search is possible while the information on them is not given to the LBS server. We performed a simulation on the proposed scheme with 16 locations where k is 3. The required time is 270 hours in a conventional desktop machine, which seems infeasible to be used in practice. However, as the progress of the hardware, the performance will be improved.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.27 no.3
• /
• pp.413-426
• /
• 2017

In this paper, we propose an adder that can be applied to data encrypted with a fully homomorphic encryption scheme and an addition method with improved performance that can be applied when adding multiple data. The proposed arithmetic adder is based on the Kogge-Stone Adder method with the optimal circuit level among the existing hardware-based arithmetic adders and suitable to apply the cryptographic SIMD (Single Instruction for Multiple Data) function on encrypted data. The proposed multiple addition method does not add a large number of data by repeatedly using Kogge-Stone Adder which guarantees perfect addition result. Instead, when three or more numbers are to be added, three numbers are added to C (Carry-out) and S (Sum) using the full-adder circuit implementation. Adding with Kogge-Stone Adder is only when two numbers are finally left to be added. The performance of the proposed method improves dramatically as the number of data increases.

• Information and Communications Magazine
• /
• v.32 no.6
• /
• pp.30-38
• /
• 2015

오류정정부호는 정보를 부호화하여 데이터 전송 과정에서 발생하는 에러를 감소시킴으로써 통신 신뢰성을 향상시킨다. 이에 따라 에러를 효율적으로 검출 및 정정할 수 있는 부호(code)가 필수적이다. 반면 암호에서는 중요한 정보를 은닉하기 위한 목적으로 비밀정보에 인위적으로 오류를 주입한다. 따라서 기밀성을 유지하기 위해서는 위와는 반대로 오류정정이 어려운 부호를 필요로 한다. 본고에서는 오류정정의 어려움으로 메시지의 비밀성은 유지되고, 덧문(trapdoor)을 가지고 있어 비밀정보가 있을 때는 메시지가 복구되는 암호학적 응용이 가능한 랜덤선형부호의 복호화 문제와 그의 응용에 대해 살펴보고자 한다. 이 문제는 암호학에서 LPN/LWE 문제로 불리며, 최근 LPN문제의 일반화된 문제인 LWE문제가 Regev에 의해 소개되면서 동형암호, 기능암호 등에 광범위하게 응용되고 있다.

• The Journal of Korean Institute of Next Generation Computing
• /
• v.15 no.4
• /
• pp.7-17
• /
• 2019

Machine learning is one of the most accurate techniques to predict and analyze various phenomena. K-means clustering is a kind of machine learning technique that classifies given data into clusters of similar data. Because it is desirable to perform an analysis based on a lot of data for better performance, K-means clustering can be performed in a model with a server that calculates the centroids of the clusters, and a number of clients that provide data to server. However, this model has the problem that if the clients' data are associated with private information, the server can infringe clients' privacy. In this paper, to solve this problem in a model with a number of clients, we propose a privacy-preserving K-means clustering method that can perform machine learning, concealing private information using homomorphic encryption.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.33 no.3
• /
• pp.375-381
• /
• 2023

Fully homomorphic encryption enables algebraic operations on encrypted data, and recently, methods for approximating non-algebraic operations such as the maximum function have been studied. However, precise approximation of max-pooling operations for four or more numbers have not been researched yet. In this study, we propose a precise max-pooling approximation method using the composition of approximate polynomials of the maximum function and theoretically analyze its precision. Experimental results show that the proposed approximate max-pooling has a small amortized runtime of less than 1ms and high precision that matches the theoretical analysis.