• Journal of the Korea Society of Computer and Information
• /
• v.24 no.11
• /
• pp.109-117
• /
• 2019

Recently, the invasion of privacy in medical information has been issued following the interest in the secondary use of mass medical information. The mass medical information is very useful information that can be used in various fields such as disease research and prevention. However, due to privacy laws such as the Privacy Act and Medical Law, this information, including patients' or health professionals' personal information, is difficult to utilize as a secondary use of mass information. To do these problem, various methods such as k-anonymity, l-diversity and differential-privacy that can be utilized while protecting privacy have been developed and utilized in this field. In this paper, we discuss the differential privacy processing of the various methods that have been studied so far, and discuss the problems of differential privacy using Laplace noise and the previously proposed differential privacy. Finally, we propose a new scheme to solve the existing problem by adding a 1-bit status field to the last column of a given data set to confirm the response to queries from analysts.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.34 no.1
• /
• pp.143-156
• /
• 2024

Federated Learning (FL) has emerged as a potent methodology for decentralized model training across multiple collaborators, eliminating the need for data sharing. Although FL is lauded for its capacity to preserve data privacy, it is not impervious to various types of privacy attacks. Differential Privacy (DP), recognized as the golden standard in privacy-preservation techniques, is widely employed to counteract these vulnerabilities. This paper makes a specific contribution by applying an existing, task-specific adaptive DP mechanism to the FL environment. Our comprehensive analysis evaluates the impact of this mechanism on the performance of a shared global model, with particular attention to varying data distribution and partitioning schemes. This study deepens the understanding of the complex interplay between privacy and utility in FL, providing a validated methodology for securing data without compromising performance.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2023.11a
• /
• pp.1250-1253
• /
• 2023

최근 정보 기술의 급격한 발전으로 스마트폰이 우리의 일상 생활에 점점 더 많이 들어오고 있으며, 사용자들은 많은 서비스들을 누릴 수 있게 되었다. 위치 기반 서비스(LBS)의 경우 스마트폰에 탑재된 위치 확인 기능을 통해 음식점 추천, 길찾기 등 개인형 맞춤 서비스를 제공하며, 사용자는 간단한 동의를 통해 자신의 위치를 LBS 서버에 전송하게 된다. 이는 사용자의 개인정보 침해의 요소가 될 수 있으며, 사용자의 민감한 정보가 공개될 수 있다. 따라서 본 논문에서는 사용자의 경로 데이터의 민감 정점을 보호하고, 통계적 질의를 할 때, 절대적으로 개인정보를 보호할 수 있는 방법을 제시한다.

• Journal of Korea Multimedia Society
• /
• v.12 no.5
• /
• pp.644-652
• /
• 2009

New communication environments such as USN, WiBro and RFID have been realized nowadays. Thus, in order to ensure security and privacy protection, various light-weight block ciphers, e.g., mCrypton, HIGHT, SEA and PRESENT, have been proposed. The block cipher mCrypton, which is a light-weight version of Crypton, is a 64-bit block cipher with three key size options (64 bits, 96 bits, 128 bits). In this paper we show that 8-round mCrypton with 128-bit key is vulnerable to related-key rectangle attack. It is the first known cryptanalytic result on mCrypton. We first describe how to construct two related-key truncated differentials on which 7-round related-key rectangle distinguisher is based and then exploit it to attack 8-round mCrypton. This attack requires $2^{45.5}$dada and $2^{45.5}$time complexities which is faster than exhaustive key search.