• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권3호
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• pp.1028-1046
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• 2022

Genome-wide association studies (GWAS) aim to find the significant genetic variants for common complex disease. However, genotype data has privacy information such as disease status and identity, which make data sharing and research difficult. Differential privacy is widely used in the privacy protection of data sharing. The current differential privacy approach in GWAS pays no attention to raw data but to statistical data, and doesn't achieve equilibrium between utility and privacy, so that data sharing is hindered and it hampers the development of genomics. To share data more securely, we propose a differential privacy preserving approach of data sharing for GWAS, and achieve the equilibrium between privacy and data utility. Firstly, a reasonable disturbance interval for the genotype is calculated based on the expected utility. Secondly, based on the interval, we get the Nash equilibrium point between utility and privacy. Finally, based on the equilibrium point, the original genotype matrix is perturbed with differential privacy, and the corresponding random genotype matrix is obtained. We theoretically and experimentally show that the method satisfies expected privacy protection and utility. This method provides engineering guidance for protecting GWAS data privacy.

• 정보화정책
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• 제29권2호
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• pp.37-65
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• 2022

이 연구는 마이데이터 이용자의 프라이버시 태도와 보호의도에 대한 프라이버시 냉소주의 4개 차원(불신, 불확실성, 무기력, 체념)의 영향 관계를 분석했다. 연구결과, 마이데이터 이용자의 인터넷 활용능력은 프라이버시 냉소주의 차원 중 '체념'에 통계적으로 유의미하게 부정적인 영향을 미치는 것으로 나타났다. 둘째, 프라이버시 위험은 프라이버시 냉소주의 차원 중 마이데이터 사업자에 대한 '불신', 프라이버시 통제에 대한 '불확실성' 및 '무기력'에 긍정적 영향을 준다. 셋째, 프라이버시 염려는 프라이버시 냉소주의 차원인 '불신', '불확실성'에 통계적으로 유의미한 긍정적 영향, '체념'은 부정적인 영향을 미치는 것으로 분석됐다. 넷째, 프라이버시 냉소주의 차원의 '체념'은 프라이버시 보호의도에 부정적인 영향을 미치는 것으로 나타났다. 종합하면, 마이데이터 이용자의 인터넷 활용능력은 프라이버시 냉소주의를 완화할 수 있는 변인이나, 프라이버시 위험과 프라이버시 염려는 프라이버시 냉소주의를 강화하는 변인으로 나타났다. 프라이버시 냉소주의 중 '체념'은 프라이버시 염려를 상쇄시키고, 프라이버시 보호의도를 낮춘다. 이는 프라이버시 노출에 대한 위험 또는 염려의 상황에서 프라이버시 냉소주의가 이러한 상황을 벗어나게 하는 인지적 메커니즘으로 기능한다는 기존 연구 결과들을 뒷받침한다.

• International Journal of Internet, Broadcasting and Communication
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• 제12권1호
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• pp.137-143
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• 2020

With the development of information and communication technology, various data have appeared and are being distributed. The use of various data has contributed to the enrichment and convenience of our lives. Data in the public areas is also growing in volume and being actively used. Public data in the field of education are also used in various ways. As the distribution and use of public data has increased, advantages and disadvantages have started to emerge. Among the various disadvantages, the privacy problem is a representative one. In this study, we deal with the privacy issues of public data in education. First, we introduce the privacy issues of public data in the education field and suggest various solutions. The various solutions include the expansion of privacy education opportunities, the need for a new privacy protection model, the provision of a training opportunity for privacy protection for teachers and administrators, and the development of a real-time privacy infringement diagnosis tool.

• 정보보호학회논문지
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• 제30권1호
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• pp.141-156
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• 2020

차분 프라이버시는 데이터 프라이버시를 보존함과 동시에 데이터를 수집 및 분석할 수 있는 기법으로써 프라이버시 보존형 데이터 활용 분야에서 널리 적용되고 있다. 이러한 차분 프라이버시의 지역적 모델인 로컬 차분 프라이버시 알고리즘은 무작위 응답을 기반으로 데이터 소유자가 직접 데이터를 가공 처리하여 공개한다. 따라서 개인은 데이터 프라이버시를 보장받을 수 있으며, 데이터 분석가는 수집된 다수의 데이터를 통해 유용한 통계적 결과값을 도출할 수 있다. 이러한 로컬 차분 프라이버시 기법은 세계적 기업인 Google, Apple, Microsoft에서 실질적으로 사용자의 데이터를 수집 및 분석할 때 활용되고 있다. 본 논문에서는 현실에 실질적으로 활용되고 있는 로컬 차분 프라이버시 기법에 대해 비교분석한다. 또한, 실제 적용 사례 연구로써 개인의 프라이버시가 결과의 신뢰성에 큰 영향을 미치는 설문 및 여론조사 시나리오를 기반으로 로컬 차분 프라이버시 기법을 적용하여 현실에서의 활용 가능성에 대해 연구한다.

• 한국컴퓨터정보학회논문지
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• 제24권9호
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• pp.43-49
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• 2019

Today, with the development of the internet of things, wearable devices related to personal health care have become widespread. Various global information and communication technology companies are developing various wearable health devices, which can collect personal health information such as heart rate, steps, and calories, using sensors built into the device. However, since individual health data includes sensitive information, the collection of irrelevant health data can lead to personal privacy issue. Therefore, there is a growing need to develop technology for collecting sensitive health data from wearable health devices, while preserving privacy. In recent years, local differential privacy (LDP), which enables sensitive data collection while preserving privacy, has attracted much attention. In this paper, we develop a technology for collecting vast amount of health data from a smartwatch device, which is one of popular wearable health devices, using local difference privacy. Experiment results with real data show that the proposed method is able to effectively collect sensitive health data from smartwatch users, while preserving privacy.

• 한국통신학회논문지
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• 제29권8C호
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• pp.1076-1085
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• 2004

최근 프라이버시 적용이 IT분야의 가장 중요한 문제의 하나로 대두되고 있다. 프라이버시 보호는 조직의 데이터 처리 시스템에 프라이버시 정책을 적용함으로써 달성 될 수 있다. 전통적인 보안 모델은 다소간 프라이버시 바인딩과 같은 기본적인 프라이버시 요구를 적용하기에 부적절하다. 본 논문은 조직에 프라이버시 정책을 적용할 수 있는 하나의 확장된 역할기반 접근제어 모델을 제안한다. 이 모델은 RBAC과 도메인-타입 적용, 그리고 프라이버시 정책을 결합함으로써 프라이버시 보호와 함께 문맥기반 접근제어를 제공한다. 프라이버시 정책은 역할에 프라이버시 등급을, 데이터에 고객의 프라이버시 선호에 따른 데이터 프라이버시 등급을 부여하는 데이터 사용 정책을 적용함으로써 달성한다. 또 이 모델을 응용에 적용하기 위하여 작은 병원 모델이 사용되었다.

• 한국IT서비스학회지
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• 제22권6호
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• pp.1-15
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• 2023

This study compares and analyzes the legal systems of Korea, the European Union, China, and the United States based on the disclosure principles and processing policies for personal data processing and provides references for seeking improvements in our legal system. Furthermore, this research aims to suggest institutional implications to overcome data transfer limitations in the upcoming digital economy. Findings on a comparative analysis of the relevant legal systems for disclosing privacy policies in four countries showed that Korea's privacy policy is under the eight principles of privacy proposed by the OECD. However, there are limitations in the current situation where personal information is increasingly transferred overseas due to direct international trade e-commerce. On the other hand, the European Union enacted the General Data Protection Regulation (GDPR) in 2016 and emphasized the transfer of personal information under the Privacy Policy. China also showed differences in the inclusion of required items in its privacy policy based on its values and principles regarding transferring personal information and handling sensitive information. The U.S. CPRA amended §1798.135 of the CCPA to add a section on the processing of sensitive information, requiring companies to disclose how they limit the use of sensitive information and limit the use of such data, thereby strengthening the protection of data providers' rights to sensitive information. Thus, we should review our privacy policies to specify detailed standards for the privacy policy items required by data providers in the era of digital economy and digital commerce. In addition, privacy-related organizations and stakeholders should analyze the legal systems and items related to the principles of personal data disclosure and privacy policies in major countries so that personal data providers can be more conveniently and accurately informed about processing their personal information.

• Journal of Information Processing Systems
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• 제20권4호
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• pp.524-534
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• 2024

The release of relational data containing personal sensitive information poses a significant risk of privacy breaches. To preserve privacy while publishing such data, it is important to implement techniques that ensure protection of sensitive information. One popular technique used for this purpose is data perturbation, which is popularly used for privacy-preserving data release due to its simplicity and efficiency. However, the data perturbation has some limitations that prevent its practical application. As such, it is necessary to propose alternative solutions to overcome these limitations. In this study, we propose a novel approach to preserve privacy in the release of relational data containing personal sensitive information. This approach addresses an intuitive, syntactic privacy criterion for data perturbation and two perturbation methods for relational data release. Through experiments with synthetic and real data, we evaluate the performance of our methods.

• International Journal of Computer Science & Network Security
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• 제23권6호
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• pp.99-106
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• 2023

Now a days, large volumes of data is accumulating in every field due to increase in capacity of storage devices. These large volumes of data can be applied with data mining for finding useful patterns which can be used for business growth, improving services, improving health conditions etc. Data from different sources can be combined before applying data mining. The data thus gathered can be misused for identity theft, fake credit/debit card transactions, etc. To overcome this, data mining techniques which provide privacy are required. There are several privacy preserving data mining techniques available in literature like randomization, perturbation, anonymization etc. This paper proposes an Enhanced Hybrid Privacy Preserving Data Mining(EHPPDM) technique. The proposed technique provides more privacy of data than existing techniques while providing better classification accuracy. The experimental results show that classification accuracies have increased using EHPPDM technique.

• 한국멀티미디어학회논문지
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• 제20권11호
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• pp.1785-1792
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• 2017

Generally, big data contains sensitive information about individuals, and thus directly releasing it for public use may violate existing privacy requirements. Therefore, privacy-preserving data publishing (PPDP) has been actively researched to share big data containing personal information for public use, while protecting the privacy of individuals with minimal data modification. Recently, with increasing demand for big data sharing in various area, there is also a growing interest in the development of software which supports a privacy-preserving data publishing. Thus, in this paper, we develops the system which aims to effectively and efficiently support privacy-preserving data publishing. In particular, the system developed in this paper enables data owners to select the appropriate anonymization level by providing them the information loss matrix. Furthermore, the developed system is able to achieve a high performance in data anonymization by using distributed Hadoop clusters.