빅데이터의 윤리적 활용을 위한 철학적 토대 : 역량 접근법을 중심으로

목광수 범한철학회 2019 汎韓哲學 Vol.95 No.4

인공지능 과학기술의 발전으로 인해, 빅데이터(Big Data)가 경제 발전의 원동력으로 주목받게 되었다. 그러나 빅데이터의 활용은 경제적 효용성뿐만 아니라, 프라이버시 침해와 같은 인간 가치의 훼손이라는 부작용 또한 상존한다. 따라서 빅데이터의 지속 가능한 활용을 위해서는 빅데이터가 윤리적으로 활용되어 정보 제공자들의 신뢰와 믿음을 얻을 필요가 있다. 왜냐하면, 빅데이터는 그 자체의 특성상, 시민들의 자발적 정보 제공에 의존하고 있다는 점에서 시민들의 지속적인 참여가 동반되어야지만 활용의 가치를 얻을 수 있기 때문이다. 본 논문은 빅데이터의 윤리적 활용을 위한 이론적 토대로 역량 접근법(capability approach)이 적합하다고 주장한다. 왜냐하면 역량 접근법은 빅데이터 활용의 윤리적 목표 설정을 위해 적합한 이론적 토대를 제공하며(2.1)절), 빅데이터라는 새로운 과학기술 논의를 포용할 수 있는 논의이기 때문이다(2.2)절). 더욱이 역량 접근법은 빅데이터 활용 과정에서 목표로 설정한 역량 증진을 도모하면서 나타날 수 있는 부작용, 예를 들면 프라이버시 침해 가능성과 같은 문제에 효과적으로 대응할 수 있는 이론적 토대를 제공하기 때문이다(3절). 본 논문은 역량 접근법이 빅데이터 활용 과정에서 나타나는 부작용에 효과적으로 대응하기 위해 개인적인 미시적 차원(3.1)절)과 사회적인 거시적 차원(3.2)절)의 통합적 구조를 제시할 수 있음을 보인다. 역량 접근법이 빅데이터의 윤리적 활용을 위한 철학적 토대로 제공되어 운용될 때, 빅데이터 기술은 인간의 가치를 고양할 수 있을 뿐만 아니라 정보 제공자인 시민들의 신뢰와 믿음을 통해 지속가능한 정보 제공이 가능해질 수 있을 것이다. For the sustainable use of Big Data, social trust and belief that Big Data is used ethically is essential. This is because Big Data in itself is dependent on the voluntary provision of personal data by citizens. For this reason, an ethical foundation to promote social trust of citizens is necessary for the sustainable use of Big Data. This paper argues that capability approach is appropriate as a theoretical framework for the ethical use of Big Data for three reasons. First, capability approach provides a suitable theoretical basis for setting ethical goals for the use of Big Data (Section 2.1)). Second, it can embrace the new science and technology discussion of Big Data (Section 2.2)). Third, capability approach can present an integrated structure of individual microscopic (Section 3.1) and social macroscopic dimensions (Section 3.2) to effectively cope with the side effects of using Big Data. When capability approach is provided and operated as a philosophical framework for the ethical use of Big Data, Big Data technologies can not only elevate human values, ​​but also can be sustainably used through the trust and trust of citizens as data providers.

빅데이터 도입의도에 미치는 영향요인에 관한 연구: 전략적 가치인식과 TOE(Technology Organizational Environment) Framework을 중심으로

가회광,김진수 한국경영정보학회 2014 Asia Pacific Journal of Information Systems Vol.24 No.4

To survive in the global competitive environment, enterprise should be able to solve various problems and find the optimal solution effectively. The big-data is being perceived as a tool for solving enterprise problems effectively and improve competitiveness with its' various problem solving and advanced predictive capabilities. Due to its remarkable performance, the implementation of big data systems has been increased through many enterprises around the world. Currently the big-data is called the 'crude oil' of the 21st century and is expected to provide competitive superiority. The reason why the big data is in the limelight is because while the conventional IT technology has been falling behind much in its possibility level, the big data has gone beyond the technological possibility and has the advantage of being utilized to create new values such as business optimization and new business creation through analysis of big data. Since the big data has been introduced too hastily without considering the strategic value deduction and achievement obtained through the big data, however, there are difficulties in the strategic value deduction and data utilization that can be gained through big data. According to the survey result of 1,800 IT professionals from 18 countries world wide, the percentage of the corporation where the big data is being utilized well was only 28%, and many of them responded that they are having difficulties in strategic value deduction and operation through big data. The strategic value should be deducted and environment phases like corporate internal and external related regulations and systems should be considered in order to introduce big data, but these factors were not well being reflected. The cause of the failure turned out to be that the big data was introduced by way of the IT trend and surrounding environment, but it was introduced hastily in the situation where the introduction condition was not well arranged. The strategic value which can be obtained through big data should be clearly comprehended and systematic environment analysis is very important about applicability in order to introduce successful big data, but since the corporations are considering only partial achievements and technological phases that can be obtained through big data, the successful introduction is not being made. Previous study shows that most of big data researches are focused on big data concept, cases, and practical suggestions without empirical study. The purpose of this study is provide the theoretically and practically useful implementation framework and strategies of big data systems with conducting comprehensive literature review, finding influencing factors for successful big data systems implementation, and analysing empirical models. To do this, the elements which can affect the introduction intention of big data were deducted by reviewing the information system's successful factors, strategic value perception factors, considering factors for the information system introduction environment and big data related literature in order to comprehend the effect factors when the corporations introduce big data and structured questionnaire was developed. After that, the questionnaire and the statistical analysis were performed with the people in charge of the big data inside the corporations as objects. According to the statistical analysis, it was shown that the strategic value perception factor and the inside-industry environmental factors affected positively the introduction intention of big data. The theoretical, practical and political implications deducted from the study result is as follows. The frist theoretical implication is that this study has proposed theoretically effect factors which affect the introduction intention of big data by reviewing the strategic value perception and environmental factors and big data related precedent studies and proposed the variables and measurement items which...

Big Data Creation Process and Measures for Utilization: Focusing on the Transportation Sector

우정욱(Jungwouk WOO) 제주대학교 관광과경영경제연구소 2021 産經論集 Vol.41 No.3

Transportation big data is not limited to the transportation sector, but is a useful resource that will bring innovation to all aspects of our lives in the future, and various R&D for its utilization is currently in progress. However, the current level of utilization of transportation big data is very limited under the existing legal system. In this study, we will investigate the meaning and problems of the use of big data in the transportation sector, and investigate the improvement tasks to expand the use of big data. Research Design, Data and Methodology: The paper used a qualitative research methodology through the literature review. In this study, first, the definition and creation process of big data were studied. Second, the significance and problems of applying big data in the transportation sector were studied. Finally, the current status of research in the transportation sector using big data was investigated, and the tasks to be improved in the process from collecting transportation big data to analysis were reviewed. Results: Big data means creating new value by fusing data collected from different purposes. In the case of using big data, the transportation sector can establish more accurate and detailed transportation policies in basic data investigation, identification of phenomena, and prediction. In order to expand the use of big data, it is important to consider who owns it, what it was collected for, what the format of the collected data is, and what should be done to use it. Conclusion: Big data is a derivative thing, but it is becoming important enough to determine the success or failure of a country depending on how it is used. However, problems such as data errors or invasion of privacy that may occur when using big data are expected. This is not just a problem in the transportation sector. When using big data, there are many problems to be solved, such as data ownership, Big Brother problems, and the implementation of smart mobility. If the advent of the big data era is taken for granted, the task from now on is how to solve these problems and share their values.

기업의 빅데이터 투자가 기업가치에 미치는 영향 연구

권영진(Young jin Kwon),정우진(Woo-Jin Jung) 한국지능정보시스템학회 2019 지능정보연구 Vol.25 No.2

According to the recent IDC (International Data Corporation) report, as from 2025, the total volume of data is estimated to reach ten times higher than that of 2016, corresponding to 163 zettabytes. then the main body of generating information is moving more toward corporations than consumers. So-called “the wave of Big-data” is arriving, and the following aftermath affects entire industries and firms, respectively and collectively. Therefore, effective management of vast amounts of data is more important than ever in terms of the firm. However, there have been no previous studies that measure the effects of big data investment, even though there are number of previous studies that quantitatively the effects of IT investment. Therefore, we quantitatively analyze the Big-data investment effects, which assists firm’s investment decision making. This study applied the Event Study Methodology, which is based on the efficient market hypothesis as the theoretical basis, to measure the effect of the big data investment of firms on the response of market investors. In addition, five sub-variables were set to analyze this effect in more depth: the contents are firm size classification, industry classification (finance and ICT), investment completion classification, and vendor existence classification. To measure the impact of Big data investment announcements, Data from 91 announcements from 2010 to 2017 were used as data, and the effect of investment was more empirically observed by observing changes in corporate value immediately after the disclosure. This study collected data on Big Data Investment related to Naver ‘s’ News’ category, the largest portal site in Korea. In addition, when selecting the target companies, we extracted the disclosures of listed companies in the KOSPI and KOSDAQ market. During the collection process, the search keywords were searched through the keywords ‘Big data construction’, ‘Big data introduction’, ‘Big data investment’, ‘Big data order’, and ‘Big data development’. The results of the empirically proved analysis are as follows. First, we found that the market value of 91 publicly listed firms, who announced Big-data investment, increased by 0.92%. In particular, we can see that the market value of finance firms, non-ICT firms, small-cap firms are significantly increased. This result can be interpreted as the market investors perceive positively the big data investment of the enterprise, allowing market investors to better understand the company’s big data investment. Second, statistical demonstration that the market value of financial firms and non - ICT firms increases after Big data investment announcement is proved statistically. Third, this study measured the effect of big data investment by dividing by company size and classified it into the top 30% and the bottom 30% of company size standard (market capitalization) without measuring the median value. To maximize the difference. The analysis showed that the investment effect of small sample companies was greater, and the difference between the two groups was also clear. Fourth, one of the most significant features of this study is that the Big Data Investment announcements are classified and structured according to vendor status. We have shown that the investment effect of a group with vendor involvement (with or without a vendor) is very large, indicating that market investors are very positive about the involvement of big data specialist vendors. Lastly but not least, it is also interesting that market investors are evaluating investment more positively at the time of the Big data Investment announcement, which is scheduled to be built rather than completed. Applying this to the industry, it would be effective for a company to make a disclosure when it decided to invest in big data in terms of increasing the market value. Our study has an academic implication, as prior research looked for the impact of Big-data investment has bee

빅데이터의 법적 보호 문제 - 영업비밀보호법에 의한 보호 가능성을 중심으로 -

이일호 법조협회 2018 法曹 Vol.67 No.1

In the era of the 4th industrial revolution, the meaning and value of data have been emphasized drastically. In recent years we are also confronted with the new coined term “big data”, which is expected to overcome the limitations and demarcation experienced by the pre-existing data itself or databases that consist of them. As a result, the government and industry tend to be active in this area, either by investing in it or by utilizing it. The more frequently big data would be applied in industry, the more plausible there could exist disputes between entities with different interests. Thus it is requires legal discussion surrounding big data, already in the earlier stage. Among other legal aspects of big data, this Paper will focus on the possibility of its legal protection. To fulfil this aim, it firstly tries to define the concept of “big data” and extract some common characteristics of it by considering the current development in the relevant technologies. On the basis of this discussion, it is examined whether and how big data can claim protection under intellectual property law, expecially according to the protection regime sui generis for database investors which has been originally developed for typical databases in the traditional sense. However, it will more focus on the possible legal protection of big data as trade secret whose protection is governed in Korea by the Trade Secret Law within the context of the unfair competition regime. Rights arising from a trade secret are distinguished from exclusive rights conferred on holders of intellectual property and deem to be, as a consequence, characterized differently. It is difficult for the trade secret to identify and specify information in secret from the other. Furthermore, many scholars and practitioners still raise questions, whether claims based on the trade secret should be regarded as a right to the exclusive utilization or just a right to exclude someone from acquiring it. Such a uncertainty makes the trade secret law the last resort for businesses who would claim ownership over objects which has not been firmly recognized as an intellectual property yet. However, trade secret law has codified some liable conditions in depth for claiming protection, although they are sometimes considered insufficient and too abstract. Thus, the question which data can be protected under trade secret law depends on the interpretation of that law. The Paper will be concluded with the result: the trade secret law is not a suitable legal institute for the legal protection of big data. The reason is that the trade secret law is from the beginning not intended to protect data, and that it is not ready yet to recognize it as protectable. However, it is expected that big data would generate a huge amount of value and the investment would continue to increase. Answers to the questions remain still open, whether such information needs to be legally protected, and how and to what extend it can be protected, more importantly how we could formulate the legal language for the big data protection. It could be helpful to gather stakeholders, scholars and practitioners and to discuss on various issues around big data. 4차산업혁명의 시대를 맞이하여 데이터가 가지는 중요성은 줄곧 강조되고 있다. 최근에는 빅데이터(big data)라는 신조어가 나와 기존 데이터 또는 데이터베이스가 가지는 가능성을 뛰어 넘는 기술진보로서 각광을 받고 있으며, 그만큼 정부나 기업에 의한 활용도 활발해질 것으로 예상된다. 빅데이터가 빈번하게 사용되면, 그만큼 이와 관련된 분쟁이 벌어질 가능성도 높아지게 된다. 따라서 빅데이터를 둘러싼 법적 쟁점에 대해서 사전에 다루어 보는 것은 무척 중요한 일이라 하겠다. 이 논문은 빅데이터가 가지고 있는 법적 측면 중에서도 그 보호 가능성에 관해 고찰하기 위한 것이다. 논문은 우선 빅데이터를 어떻게 정의할 수 있는지, 또 현재의 발달상황을 고려해 보았을 때, 빅데이터가 가지고 있는 특징은 무엇인지 살펴보고자 한다. 이러한 분석을 토대로 빅데이터가 과연 현재의 지식재산권법(특히 저작권법) 및 불법행위법에 의해 보호될 수 있는지에 대해 고려해볼 것이다. 여기서는 무엇보다 부정경쟁방지 및 영업비밀보호에 관한 법률을 통해 빅데이터가 보호될 수 있는지에 대해 집중하고자 한다. 영업비밀은 여타 지식재산들과는 다른 성격을 가진다. 영업비밀로 보호되는 정보를 특정하기 어려운 것은 물론이고, 그 보호가 단지 방어권에 머무는지, 적극적인 이용권한에까지 이르는지에 대해 아직까지도 모호한 상황이다. 이러한 모호성 때문에 영업비밀보호법제는 때때로 명확하게 지식재산으로 보호되지 못하는 것을 보호하기 위한 마지막 보루처럼 여겨지기도 한다. 그러나 동 법제는 추상적이기는 하지만, 영업비밀로 보호되기 위해 갖추어야 할 요건들과 침해로 인정되는 행위를 비교적 구체적으로 제시하고 있다. 그 결과 빅데이터가 영업비밀이 되는지 여부는 이 조건들에 의해서 결정될 수밖에 없다. 결론적으로 영업비밀보호법제는 현재로서 빅데이터를 보호함에 있어 적합한 법제도라고 할 수는 없다. 이는 영업비밀보호법이 애초에 빅데이터를 전제로 만들어진 것이 아니기 때문이기도 하다. 빅데이터가 경제적 가치를 가지고, 산업계에서 그 가치를 인정받는 것은 사실이지만, 이것을 법적으로 보호할 것인지, 또 어떻게 얼마만큼 보호할 것인지는 또 다른 문제라고 할 수 있다. 이해관계를 달리하는 다양한 사람들, 법학자 및 법실무자들이 함께 모여 적합한 보호방안에 대해 심도 있는 논의를 전개할 필요가 있다.

A Study on Open API of Securities and Investment Companies in Korea for Activating Big Data

Gui Yeol Ryu 한국인터넷방송통신학회 2019 Journal of Advanced Smart Convergence Vol.8 No.2

Big data was associated with three key concepts, volume, variety, and velocity. Securities and investment services produce and store a large data of text/numbers. They have also the most data per company on the average in the US. Gartner found that the demand for big data in finance was 25%, which was the highest. Therefore securities and investment companies produce the largest data such as text/numbers, and have the highest demand. And insurance companies and credit card companies are using big data more actively than banking companies in Korea. Researches on the use of big data in securities and investment companies have been found to be insignificant. We surveyed 22 major securities and investment companies in Korea for activating big data. We can see they actively use AI for investment recommend. As for big data of securities and investment companies, we studied open API. Of the major 22 securities and investment companies, only six securities and investment companies are offering open APIs. The user OS is 100% Windows, and the language used is mainly VB, C#, MFC, and Excel provided by Windows. There is a difficulty in real-time analysis and decision making since developers cannot receive data directly using Hadoop, the big data platform. Development manuals are mainly provided on the Web, and only three companies provide as files. The development documentation for the file format is more convenient than web type. In order to activate big data in the securities and investment fields, we found that they should support Linux, and Java, Python, easy-to-view development manuals, videos such as YouTube.

증거기반 정책에서의 빅데이터에 관한 연구

김선영 ( Sunyoung Kim ) 한국정책학회 2020 韓國政策學會報 Vol.29 No.1

과학기술과 컴퓨터 과학의 발전과 함께 인터넷과 센서로 연결된 사회에서는 사람들의 행위, 상호작용, 그리고 경제적 상황 등에서 다양한 종류, 다양한 형태의 엄청난 양의 데이터가 쉼 없이 생성·저장되고 있다. 이를 빅데이터라고 한다. 다양한 영역에서 그 활용이 개발되고 있는 빅데이터는 행정기관의 효율성 제고와 정책수단으로도 긍정적 평가를 받고 있다. 빅데이터는 기계학습을 통해 목적에 맞게 활용될 수 있다. 특히 종래의 전통적 데이터를 통한 실증연구가 정책의 근거로 사용되는 것처럼, 빅데이터도 증거기반 의사결정에서 중요한 도구로 활용된다. 증거기반 의사결정에서 기계학습방법을 통한 빅데이터 분석은 데이터 하위 모집단을 두루 분석할 수 있게 해 이전에 데이터를 통해 찾아볼 수 없었던 편향된 현상뿐만 아니라 전반적인 사회현상을 구체적으로 살펴볼 수 있게 한다. 동시에 데이터의 메타성으로 인해 더욱 정확한 예측을 가능하게도 한다. 이는 여러 나라에서 개발·적용되고 있는 사례를 통해 알 수 있다. 그러나 최근 빅데이터를 기반으로 수립된 정책과 현실 적용 간의 차이는 정책에서의 빅데이터 사용에 대한 우려의 원인이 되고 있다. 이에 대해 전통적인 데이터로부터 빅데이터를 이해하고 빅데이터 분석을 위한 기계학습방법에 관한 정리를 통해 근거기반 정책 결정 도구로 빅데이터의 활용에 관한 함의를 얻는 데 그 목적이 있다. With the development of science and computer science, huge amounts of data of various kinds and types of people's behavior, interaction, and social-economic situation. are continually being generated and stored in the society connected with the internet and sensors. This is called big data. Big data can be used depending on the research purpose by using machine learning. The big data, which is actively used by the private sector, is also positively evaluated as a means of improving the efficiency of government agencies’ work and policy means. In particular, just as empirical research using traditional data is used as evidence for a policy, big data can be used in evidence-based decision making. This is because it allows researchers to investigate not only human society that has not been experienced before but also overall social phenomena by analyzing big data through the machine learning method that can analyze commonality and heterogeneity of data sub-populations and entire population in detail. At the same time, the massive volume of data makes it possible to make more accurate predictions. However, in most studies on big data related to making decisions or policies, the analysis approach of big data tends to be based on the traditional data approach method. As a result, it is pointed out that the policy results of big data analysis in the evidence-based policy perspective are not satisfied. This study discovered that the result is a lack of understanding of big data. The purpose of this study is to understand the big data concept and characteristics from the traditional data and to obtain the implications for the use of big data as an evidence-based policy means with the understanding of the machine learning method for big data analysis.

Comparative Study of Big Data Computing and Storage Tools : A Review

Bakshi Rohit Prasad,Sonali Agarwal 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.1

As a result of tremendous rise in internet usage like social media and forums, mail systems, scholarly and research articles, daily online transactions from multiple sources like health care systems, meteorological and environmental organizations etc., the data collected has shoot up exponentially. This vast collection of data, called Big Data, has caused the traditional tools incompetent for managing it from either of storage, computing or analytical perspective. There is an immense need of architectures, platforms, tools, techniques and algorithms to handle Big Data. The available technologies deal with two broad aspects related to Big Data that are Big Data Storage Management and Big Data Computing, focused to overcome various challenges such as scalability, faster processing speed, multiple format data processing, availability, faster response time and analytics etc. This paper reviews recent trends of storage and computing tools with their relative capabilities, limitations and environment they are suitable to work with.

빅데이터의 효과적인 처리 및 활용을 위한 클라이언트-서버 모델 설계

박대서(Dae Seo Park),김화종(Hwa Jong Kim) 한국지능정보시스템학회 2016 지능정보연구 Vol.22 No.4

Recently, big data analysis has developed into a field of interest to individuals and non-experts as well as companies and professionals. Accordingly, it is utilized for marketing and social problem solving by analyzing the data currently opened or collected directly. In Korea, various companies and individuals are challenging big data analysis, but it is difficult from the initial stage of analysis due to limitation of big data disclosure and collection difficulties. Nowadays, the system improvement for big data activation and big data disclosure services are variously carried out in Korea and abroad, and services for opening public data such as domestic government 3.0 (data.go.kr) are mainly implemented. In addition to the efforts made by the government, services that share data held by corporations or individuals are running, but it is difficult to find useful data because of the lack of shared data. In addition, big data traffic problems can occur because it is necessary to download and examine the entire data in order to grasp the attributes and simple information about the shared data. Therefore, We need for a new system for big data processing and utilization. First, big data pre-analysis technology is needed as a way to solve big data sharing problem. Pre-analysis is a concept proposed in this paper in order to solve the problem of sharing big data, and it means to provide users with the results generated by pre-analyzing the data in advance. Through preliminary analysis, it is possible to improve the usability of big data by providing information that can grasp the properties and characteristics of big data when the data user searches for big data. In addition, by sharing the summary data or sample data generated through the pre-analysis, it is possible to solve the security problem that may occur when the original data is disclosed, thereby enabling the big data sharing between the data provider and the data user. Second, it is necessary to quickly generate appropriate preprocessing results according to the level of disclosure or network status of raw data and to provide the results to users through big data distribution processing using spark. Third, in order to solve the problem of big traffic, the system monitors the traffic of the network in real time. When preprocessing the data requested by the user, preprocessing to a size available in the current network and transmitting it to the user is required so that no big traffic occurs. In this paper, we present various data sizes according to the level of disclosure through pre - analysis. This method is expected to show a low traffic volume when compared with the conventional method of sharing only raw data in a large number of systems. In this paper, we describe how to solve problems that occur when big data is released and used, and to help facilitate sharing and analysis. The client-server model uses SPARK for fast analysis and processing of user requests. Server Agent and a Client Agent, each of which is deployed on the Server and Client side. The Server Agent is a necessary agent for the data provider and performs preliminary analysis of big data to generate Data Descriptor with information of Sample Data, Summary Data, and Raw Data. In addition, it performs fast and efficient big data preprocessing through big data distribution processing and continuously monitors network traffic. The Client Agent is an agent placed on the data user side. It can search the big data through the Data Descriptor which is the result of the pre-analysis and can quickly search the data. The desired data can be requested from the server to download the big data according to the level of disclosure. It separates the Server Agent and the client agent when the data provider publishes the data for data to be used by the user. In particular, we focus on the Big Data Sharing, Distributed Big Data Processing, Big Traffic problem, and construct the detailed module of

중국 빅데이터 거래에 관한 법적 고찰 -정보법을 중심으로-

김군 ( Jin Jun ) 중앙대학교 문화미디어엔터테인먼트법연구소 2018 문화.미디어.엔터테인먼트 법 Vol.12 No.2

정보기술과 경제사회의 융합으로 인하여 빅데이터는 급속히 발전하였다. 중국정보통 신연구원에서 조사 및 발표한 결과에 따르면 2017년 중국 빅데이터 산업의 총 규모가 4700억 위안에 달하며, 전년 동기 대비 약 30% 증가하였다. 빅데이터 산업이 새로운 경제 성장 엔진이되고 있으며 정보산업 미래의 패턴에 중요한 역할을 할 것으로 판단된다. 빅데이터 거래가 왕성하게 발전하고 있지만 빅데이터 관련된 단독입법이 아직 미비된 실정이며, 실무상 <민법총칙>, <계약법>, <저작권법>, <반부정당경쟁법>, 정보보호 관련 법률에 의하여 빅데이터 거래를 규율하고 있다. 본문은 중국의 빅데이터 거래에 대한 법적 근거를 제시하기 위하여 빅데이터 거래소, 빅데이터 거래상품의 종류 그리고 빅데이터 거래과련 법규, 빅데이터 거래소규칙에 대한 내용을 소개하였다. 또한 빅데이터의 귀속문제에 있어서 명시적인 규정은 없으나 실무상 가공된 데이터에 대하여는 약정이 있는 경우 약정에 의하며, 약정이 없는 경우 해당 데이터는 가공(조성)한 자에게 귀속되는 것으로 판단하고 있다. 빅데이터 유통에 있어서, 정보수집자는 개인으로부터 정보를 수집할 경우 해당 정보의 사용방식, 목적, 범위를 명시하고 피수집자의 동의를 받아야한다. 그리고 본문의 마지막부분에는 빅데이터와 관련된 판례 2편을 검토하였다. 판결요지에 따르면 허가 없이 타인이 합법적으로 수집한 빅데이터를 이용할 경우 부정경쟁행위에 해당된다. 또한 cookie 사건에서는 개인 정보 이용에 있어서 프라이버시권침해 기준을 확립하였다. The integration of information technology and economic society promotes the rapid development of Big Data. According to the survey conducted by the China Academy of Information and Communications Technology that the size of China's Big Data industry was 470 billion RMB in 2017, demonstrating a 30% year-on-year increase. There can be no doubt that Big Data industry is becoming a new economic growth engine and will play significant role for the future patterns of the information industry. Although Big Data transactions have developed vigorously, there is still no specific rules to regulate it. In practice, Big Data transaction is regulated by The General Rules of the Civil Law, Contact Law, Copyright Law, Law of the PRC against Unfair Competition, and the Information Protection of related regulations and policies. In order to provide grounds for Big Data transactions, this article introduces the general situation of China's Big Data Exchanges and the types of Big Data transactions, as well as the laws related to Big Data and the trading rules of Big Date Exchanges in China. In fact, there is no explicit rules for attribution of big data, and the attribution of Big Data is determined by agreement in legal practice generally. If there is no agreement in previous, the right of Big Data should be attributed to the Data collector (or creator). In the aspect of Big Data circulations, the information collector, which shall specify the using of information method, the extent and purpose of information to the individuals, shall obtain the individuals consent in advance. Finally, this article also review two cases related Big Data. According to the ruling, without permission of using Data, which is legally collected by others, is an unfair competition. In the case of cookies, the court established a standard for using personal information to infringe on the privacy rights of individuals.