• Journal of the Korea Society of Computer and Information
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• v.29 no.5
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• pp.93-100
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• 2024

With the advent of the Fourth Industrial Revolution, cutting-edge technologies such as artificial intelligence, big data, the Internet of Things, and cloud computing are driving innovation across industries. These technologies are generating massive amounts of data that many companies are leveraging. However, there is a notable reluctance among users to share sensitive information due to the privacy risks associated with collecting personal data. This is particularly evident in the healthcare sector, where the collection of sensitive information such as patients' medical conditions poses significant challenges, with privacy concerns hindering data collection and analysis. This research presents a novel technique for collecting and analyzing medical data that not only preserves privacy, but also effectively extracts statistical information. This method goes beyond basic data collection by incorporating a strategy to efficiently mine statistical data while maintaining privacy. Performance evaluations using real-world data have shown that the propose technique outperforms existing methods in extracting meaningful statistical insights.

• Smart Media Journal
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• v.4 no.4
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• pp.33-38
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• 2015

Cloud storage server do not detect duplication of conventionally encrypted data. To solve this problem, Convergent Encryption has been proposed. Recently, various client-side deduplication technology has been proposed. However, this propositions still cannot solve the security problem. In this paper, we suggest a secure source-based deduplication technology, which encrypt data to ensure the confidentiality of sensitive data and apply proofs of ownership protocol to control access to the data, from curious cloud server and malicious user.

• Journal of Digital Convergence
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• v.14 no.2
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• pp.175-181
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• 2016

Cloud storage servers do not detect duplication of conventionally encrypted data. To solve this problem, convergent encryption has been proposed. Recently, various client-side deduplication technology has been proposed. However, this propositions still cannot solve the security problem. In this paper, we suggest a secure source-based deduplication technology, which encrypt data to ensure the confidentiality of sensitive data and apply proofs of ownership protocol to control access to the data, from curious cloud server and malicious user.

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.408-411
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• 2024

원격 환경에서의 안전한 데이터 처리를 위한 기술 중 동형암호는 암호화된 데이터 간의 연산을 통한 프라이버시 보존형 연산이 가능하여 최근 딥러닝 연산을 동형암호로 수행하고자 하는 연구가 활발히 진행되고 있다. 그러나 동형암호는 신경망에 존재하는 비산술 활성화함수를 직접적으로 연산할 수 없어 다항함수로 대체하여 연산해야만 하는데, 이로 인해 모델의 정확도가 하락하거나 과도한 연산 부하가 발생하는 등의 비효율성 문제가 발생한다. 본 연구에서는 모델 내의 활성화함수를 서로 다르게 근사하는 접근을 순환신경망(Recurrent Neural Network, RNN)에 적용하여 효율적인 동형암호 연산을 수행하는 방법을 제안하고자 한다.

• Journal of the Korea Society for Simulation
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• v.24 no.4
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• pp.153-161
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• 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 Digital Contents Society
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• v.18 no.8
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• pp.1671-1676
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• 2017

In theses days, data has been explosively generated in diverse industrial areas. Accordingly, many industries want to collect and analyze these data to improve their products or services. However, collecting user data can lead to significant personal information leakage. Local differential privacy (LDP) proposed by Google is the state-of-the-art approach that is used to protect individual privacy in the process of data collection. LDP guarantees that the privacy of the user is protected by perturbing the original data at the user's side, but a data collector is still able to obtain population statistics from collected user data. However, the prevention of leakage of personal information through such data perturbation mechanism may cause the significant reduction in the data utilization. Therefore, the degree of data perturbation in LDP should be set properly depending on the data collection and analysis purposes. Thus, in this paper, we develop the simulation tool which aims to help the data collector to properly chose the degree of data perturbation in LDP by providing her/him visualized simulated results with various parameter configurations.

• 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.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.3
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• pp.685-692
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• 2019

Smart grid enables efficient power management by allowing real-time awareness of electricity flows through two-way communication. Despite its various advantages, threats to user privacy caused by frequent meter reading hinder prosperous deployment of smart grid. In this paper, we propose a privacy-preserving aggregation method exploiting fully homomorphic encryption (FHE). Specifically, it achieves privacy-preserving fine-grained aggregation of electricity usage for smart grid customers in multiple electrical source environments, while further enhancing efficiency through SIMD-style operations simultaneously. Analysis of our scheme demonstrates the suitability in next-generation smart grid environment where the customers select and use a variety of power sources and systematic metering and control are enabled.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.522-524
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• 2023

위치 기반 서비스(LBS)의 확산으로 다양한 분야에서 활용할 수 있는 많은 양의 경로 데이터가 생성되고 있다. 하지만 공격자가 경로 데이터를 통해 잠재적으로 사용자의 개인정보를 유추할 수 있다는 문제점이 존재한다. 따라서 경로 데이터의 프라이버시를 보존하며 유용성을 유지할 수 있는 GAN(Generative Adversarial Network)을 사용한 많은 연구가 진행되고 있다. 그러나 GAN은 생성된 결과물을 제어하지 못한다는 한계점을 가지고 있다. 본 논문에서는 ACGAN(Auxiliary classifier GAN)을 통해 생성된 결과물을 제어함으로써 경로 데이터의 민감한 정점을 숨기고, Attention mechanism을 결합하여 높은 유용성과 익명성을 제공하는 합성 경로 생성 모델인 TAP-GAN(Trajectory attention and protection-GAN)을 제안한다. 또한 모델의 성능을 입증하기 위해 유용성 및 익명성 실험을 진행하고, 선행 연구 모델과의 비교를 통해 TAP-GAN이 경로 데이터의 유용성을 보장하면서 사용자의 프라이버시를 효과적으로 보호할 수 있음을 확인하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.419-422
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• 2024

최근 인공지능 기술의 발전과 함께 기계학습과 빅데이터를 융합한 서비스가 증가하게 되었고, 무분별한 데이터 수집과 학습으로 인한 개인정보 유출 위험도가 커졌다. 따라서 프라이버시를 보호하면서 기계학습을 수행할 수 있는 기술이 중요해졌다. 동형암호 기술은 정보 주체자의 개인정보 기밀성을 유지하면서 기계학습을 할 수 있는 방법 중 하나이다. 그러나 평문 크기에 비례하여 암호문 크기와 연산 결과의 노이즈가 커지는 동형암호의 특징으로 인해 기계학습 모델의 예측 정확도가 감소하고 학습 시간이 오래 소요되는 문제가 발생한다. 본 논문에서는 부분 동형암호화된 데이터셋으로 로지스틱 회귀 모델을 학습할 수 있는 기법을 제안한다. 실험 결과에 따르면 제안하는 기법이 종래 기법보다 예측 정확도를 59.4% 향상시킬 수 있었고, 학습 소요 시간을 63.6% 개선할 수 있었다.