• The Journal of the Convergence on Culture Technology
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• v.2 no.4
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• pp.71-76
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• 2016

In this study, de-identification policies of the US, the UK, Japan, China and Korea are compared to suggest a future direction of de-identification regulations and a method for vitalizing the big data industry. Efficiently using the de-identification technology and the standard of adequacy evaluation contributes to using personal information for the industry to develop services and technology while not violating the right of private lives and avoiding the restrictions specified in the Personal Information Protection Act. As a counteraction, the re-identification issue may occur, for re-identifying each person as a de-identified data collection. From the perspective of business, it is necessary to mitigate schemes for discarding some regulations and using big data, and also necessary to strengthen security and refine regulations from the perspective of information security.

• Journal of Digital Convergence
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• v.19 no.8
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• pp.205-217
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• 2021

Decentralized SSI(Self Sovereign Identity) has become an alternative to a new digital identity solution, but an efficient de-identification technique has not been proposed due to the unique algorithmic characteristics of data transactions. In this study, to ensure the decentralized operation of SSI, we propose a de-identification technique that does not remove identifiers by restructuring the verification results of ZKP (Zero Knowledge Proof) into a form that can be provided to the outside by the verifier. In addition, it is possible to provide restructured de-identification data without the consent of data subject by proposing the concept of differential sovereignty management for each entity participating in verification. As a result, the proposed model satisfies the domestic personal information protection law in a decnetralized SSI, in addition provides secure and efficient de-identification processing and sovereignty management.

• Convergence Security Journal
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• v.19 no.1
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• pp.41-48
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• 2019

In the era of Internet of Things and Artificial Intelligence, it has become essential to digitize mass data, which leads 'data-driven economy'. Digitalized personal data can be easily collected, stored, duplicated and analyzed. As ICT technology is evolving the concept of traditional personal data has changed. The United States, the European Union, Japan, Korea and many countries have introduced new concept of personal data into law such as de-identification, anonymization, and pseudonymization to protect and utilize digitalized personal information. These concepts are distinguishable depending on countries. Therefore, this study will be done by researching and analyzing personal data related policies of several countries. Based on this study, this paper will suggest policy on di-identification to draw the right balance between personal data protection and use, which contributes to the development of digital economy.

• Journal of the Korea Society of Computer and Information
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• v.27 no.6
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• pp.53-63
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• 2022

Recently, as digital transformation due to the COVID-19 pandemic accelerates, data to improve individual quality of life is being used in large quantities, and more reinforced non-identification processing procedures are required to utilize the most valuable personal information among data. In Korea, procedures for de-identification measures are presented through amendments to laws and guidelines, but there is no methodology to measure the level of de-identification in the field due to ambiguous processing standards and subjective risk measurement methods. This paper compares and analyzes the current status of policy and guidelines related to de-identification measures proposed at home and abroad to derive complementary points, suggests a data context-based risk measurement method centered on pseudonymized information processing, and verifies its validity. As a result of verification through Delphi survey and focus group interview (FGI), it was confirmed that the need for the proposed methodology and the validity of the indicators were high.

• Convergence Security Journal
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• v.22 no.4
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• pp.85-97
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• 2022

With the development of big data technology, data is rapidly entering a hyperconnected intelligent society that accelerates innovative growth in all industries. The convergence industry, which holds and utilizes various high-quality data, is becoming a new growth engine, and big data is fused to various traditional industries. In particular, in the medical field, structured data such as electronic medical record data and unstructured medical data such as CT and MRI are used together to increase the accuracy of disease prediction and diagnosis. Currently, the importance and size of unstructured data are increasing day by day in the medical industry, but conventional data security technologies and policies are structured data-oriented, and considerations for the security and utilization of unstructured data are insufficient. In order for medical treatment using big data to be activated in the future, data diversity and security must be internalized and organically linked at the stage of data construction, distribution, and utilization. In this paper, the current status of domestic and foreign data security systems and technologies is analyzed. After that, it is proposed to add unstructured data-centered de-identification technology to the guidelines for unstructured data and technology application cases in the industry so that unstructured data can be actively used in the medical field, and to establish standards for judging personal information for unstructured data. Furthermore, an object feature-based identification ID that can be used for unstructured data without infringing on personal information is proposed.

• Convergence Security Journal
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• v.19 no.5
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• pp.25-36
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• 2019

With the development of information and communication technology, hospitals that electronically process and manage medical information of patients are increasing. However, if medical information is processed electronically, there is still room for infringing personal information of the patient or medical staff. Accordingly, in 2017, the International Organization for Standardization (ISO) published ISO TS 25237 Health Information - Pseudonymization[1]. In this paper, we examine the re - identification process based on ISO TS 25237, the procedure and the problems of our proposed method. In addition, we propose a new processing scheme that adds a re-identification procedure to our secure differential privacy method [2] by keeping a mapping table between de-identified data sets and original data as ciphertext. The proposed method has proved to satisfy the requirements of ISO TS 25237 trust service providers except for some policy matters.

• ETRI Journal
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• v.37 no.1
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• pp.186-196
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• 2015

In resource-constrained, low-cost, radio-frequency identification (RFID) sensor-based mobile ad hoc networks (MANETs), ensuring security without performance degradation is a major challenge. This paper introduces a novel combination of steps in lightweight protocol integration to provide a secure network for RFID sensor-based MANETs using error-correcting codes (ECCs). The proposed scheme chooses a quasi-cyclic ECC. Key pairs are generated using the ECC for establishing a secure message communication. Probability analysis shows that code-based identification; key generation; and authentication and trust management schemes protect the network from Sybil, eclipse, and de-synchronization attacks. A lightweight model for the proposed sequence of steps is designed and analyzed using an Alloy analyzer. Results show that selection processes with ten nodes and five subgroup controllers identify attacks in only a few milliseconds. Margrave policy analysis shows that there is no conflict among the roles of network members.

• Journal of Digital Convergence
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• v.18 no.4
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• pp.325-340
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• 2020

Today's rapidly developing health technology is accumulating vast amounts of data through medical devices based on the Internet of Things in addition to data generated in hospitals. The collected data is a raw material that can create a variety of values, but our society lacks legal and institutional mechanisms to support medical Big Data. Therefore, in this study, we looked at four major factors that hinder the use of medical Big Data to find ways to enhance use of the Big Data based healthcare industry, and also derived implications for expanding domestic medical Big Data by identifying foreign policies and technological trends. As a result of the study, it was concluded that it is necessary to improve the regulatory system that satisfies the security and usability of healthcare Big Data as well as establish Big Data governance. For this, it is proposed to refer to the Big Data De-identification Guidelines adopted by the United States and the United Kingdom to reorganize the regulatory system. In the future, it is expected that it will be necessary to have a study that has measures of the conclusions and implications of this study and to supplement the institutional needs to play a positive role in the use of medical Big Data.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.2
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• pp.287-304
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• 2020

There have been a number of legal & policy studies on the affecting factors of big data utilization, but empirical research on the composition factors of personal information regulation or data combination, which acts as a constraint, has been hardly done due to the lack of relevant statistics. Therefore, this study empirically explores the priority of personal information regulation factors and data combination factors that influence big data utilization through Delphi Analysis. As a result of Delphi analysis, personal information regulation factors include in order of the introduction of pseudonymous information, evidence clarity of personal information de-identification, clarity of data combination regulation, clarity of personal information definition, ease of personal information consent, integration of personal information supervisory authority, consistency among personal information protection acts, adequacy punishment intensity in case of violation of law, and proper penalty level when comparing EU GDPR. Next, data combination factors were examined in order of de-identification of data combination, standardization of combined data, responsibility of data combination, type of data combination institute, data combination experience, and technical value of data combination. These findings provide implications for which policy tasks should be prioritized when designing personal information regulations and data combination policies to utilize big data.

• ALGAE
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• v.33 no.3
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• pp.269-278
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• 2018

Ocean warming can have significant negative impacts on population genetic diversity, local endemism and geographical distribution of a wide range of marine organisms. Thus, the identification of conservation units with high risk of extinction becomes an imperative task to assess, monitor, and manage marine biodiversity for policy-makers. Here, we surveyed population structure and genetic variation of the red seaweed Gracilaria vermiculophylla along the coast of China using genome-based amplified fragment length polymorphism (AFLP) scanning. Regardless of analysis methods used, AFLP consistently revealed a south to north genetic isolation. Populations at the southern coast of China showed unique genetic variation and much greater allelic richness, heterozygosity, and average genetic diversity than the northern. In particular, we identified a geographical barrier that may hinder genetic exchange between the two lineages. Consequently, the characterized genetic lineage at the southern coast of China likely resulted from the interplay of post-glacial persistence of ancestral diversity, geographical isolation and local adaptation. In particular, the southern populations are indispensable components to explore evolutionary genetics and historical biogeography of G. vermiculophylla in the northwestern Pacific, and the unique diversity also has important conservation value in terms of projected climate warming.