• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.2
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• pp.417-428
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• 2018

The intelligent video surveillance environment is a system that extracts various information about a video object and enables automated processing through the analysis of video data collected in CCTV. However, since the privacy exposure problem may occur in the process of intelligent video surveillance, it is necessary to take a security measure. Especially, video metadata has high vulnerability because it can include various personal information analyzed based on big data. In this paper, we propose a COP-Transformation scheme to protect video metadata. The proposed scheme is advantageous in that it greatly enhances the security and efficiency in processing the video metadata.

• 전자공학회논문지 IE
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• v.46 no.2
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• pp.22-32
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• 2009

In this article, the protection system on wireless mobile communication circumstance for the information providers and the users is recommended. Because of its usefulness and convenience, the users of the wireless mobile communication are growing explosively. However, the function of protecting data systems is not secured enough so, personal information may disclose to the outside, regardless of one's intention. Therefore, the contents protection system, which can provide information to the user or hide it depending on user's identity, is suggested. If so, the providers and the users can trust each other for interchanging information, also the users may safely use contents menu whatever they want by doing simple certification process.

• The Transactions of the Korea Information Processing Society
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• v.13 no.2
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• pp.76-90
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• 2024

Although artificial intelligence (AI) can be utilized in various domains such as smart city, healthcare, it is limited due to concerns about the exposure of personal and sensitive information. In response, the concept of distributed machine learning has emerged, wherein learning occurs locally before training a global model, mitigating the concentration of data on a central server. However, overall learning phase in a collaborative way among multiple participants poses threats to data privacy. In this paper, we systematically analyzes recent trends in privacy protection within the realm of distributed machine learning, considering factors such as the presence of a central server, distribution environment of the training datasets, and performance variations among participants. In particular, we focus on key distributed machine learning techniques, including horizontal federated learning, vertical federated learning, and swarm learning. We examine privacy protection mechanisms within these techniques and explores potential directions for future research.

• The Korean Society of Law and Medicine
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• v.21 no.3
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• pp.145-178
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• 2020

This paper examines discussions surrounding cognitive liberty, neuro-privacy, and mental integrity from the perspective of Neuro-rights. The right to control one's neurological data entails self-determination of collection and usage of one's data, and the right to object to any way such data may be employed to negatively impact oneself. As innovations in neurotechnologies bear benefits and downsides, a novel concept of the neuro-rights has been suggested to protect individual liberty and rights. In Oct. 2020, the Chilean Senate presented the 'Proyecto de ley sobre neuroderechos' to promote the recognition and protection of neuro-rights. This new bill defines all data obtained from the brain as neuronal data and outlaws the commerce of this data. Neurotechnology, especially when paired with big data and artificial intelligence, has the potential to turn one's neurological state into data. The possibility of inferring one's intent, preferences, personality, memory, emotions, and so on, poses harm to individual liberty and rights. However, the collection and use of neural data may outpace legislative innovation in the near future. Legal protection of neural data and the rights of its subject must be established in a comprehensive way, to adapt to the evolving data economy and technical environment.

• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.93-101
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• 2016

As the Android smart phones become more popular, applications that handle users' personal data such as IDs or passwords and those that handle data directly related to companies' income such as in-game items are also increasing. Despite the need for such information to be protected, it can be modified by malicious users or leaked by attackers on the Android. The reason that this happens is because debugging functions of the Linux, base of the Android, are abused. If an application uses debugging functions, it can access the virtual memory of other applications. To prevent such abuse, access controls should be reinforced. However, these functions have been incorporated into Android O.S from its Linux base in unmodified form. In this paper, based on an analysis of both existing memory access functions and the Android environment, we proposes a function that verifies thread group ID and then protects against illegal use to reinforce access control. We conducted experiments to verify that the proposed method effectively reinforces access control. To do that, we made a simple application and modified data of the experimental application by using well-established memory editing applications. Under the existing Android environment, the memory editor applications could modify our application's data, but, after incorporating our changes on the same Android Operating System, it could not.

• Industry Promotion Research
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• v.8 no.2
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• pp.21-27
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• 2023

This study studies the policies and use cases of the government and the financial sector for artificial intelligence, and the future policy tasks of the financial sector. want to derive According to Gartner, noteworthy technologies leading the financial industry in 2022 include 'generative AI', 'autonomous system', 'Privacy Enhanced Computation (PEC) was selected. The financial sector is developing new technologies such as artificial intelligence, big data, and blockchain. Developments are spurring innovation in the financial sector. Data loss due to the spread of telecommuting after the corona pandemic As interests in sharing and personal information protection increase, companies are expected to change in new digital technologies. Global financial companies also utilize new digital technology to develop products or manage and operate existing businesses. I n order to promote process innovation, I T expenses are being expanded. The financial sector utilizes new digital technology to prevent money laundering, improve work efficiency, and strengthen personal information protection. are applying In the era of Big Blur, where the boundaries between industries are disappearing, the competitive edge in the challenge of new entrants In order to preoccupy the market, financial institutions must actively utilize new technologies in their work.

• Spatial Information Research
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• v.23 no.6
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• pp.19-29
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• 2015

This study was conducted with the purpose of suggesting the improvement plan of political for activating the Geo-Spatial Big Data Services. To this end, we were review the previous research for Geo-Spatial Big Data and analysis domestic and foreign Geo-Spatial Big Data propulsion system and policy enforcement situation. As a result, we have deduced the problem of insufficient policy of reaction for future Geo-Spatial Big Data, personal information protection and political basis service activation, relevant technology and policy, system for Geo-Spatial Big Data application and establishment, low leveled open government data and sharing system. In succession, we set up a policy direction for solving derived problems and deducted 5 policy issues : setting up a Geo-Spatial Big Data system, improving relevant legal system, developing technic related to Geo-Spatial Big Data, promoting business supporting Geo-Spatial Big Data, creating a convergence sharing system about public DB.

• Journal of Digital Convergence
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• v.18 no.6
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• pp.17-32
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• 2020

This study analyzes the regulatory issues of stakeholders, the firm, the government, and the individual, in the data industry using the principal-agent theory. While the importance of data driven economy is increasing rapidly, policy regulations and restrictions to use data impede the growth of data industry. We applied descriptive case analysis methodology using principal-agent theory. From our analysis, we found several meaningful results. First, key policy actors in data industry are data firms and the government among stakeholders. Second, two major concerns are that firms frequently invade personal privacy and the global companies obtain monopolistic power in data industry. This paper finally suggests policy and strategy in response to regulatory issues. The government should activate the domestic agent system for the supervision of global companies and increase data protection. Companies need to address discriminatory regulatory environments and expand legal data usage standards. Finally, individuals must embody an active behavior of consent.

• Journal of Digital Convergence
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• v.13 no.9
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• pp.129-136
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• 2015

We investigate the underlying structure of big data research domain, which is diversified and complicated using bottom-up approach. For that purpose, we derive a set of articles by searching "big data" through the Korea Citation Index System provided by National Research Foundation of Korea. With some preprocessing on the author-provided keywords, we analyze bibliometric data such as author-provided keywords, publication year, author, and journal characteristics. From the analysis, we both identify major sub-domains of big data research area and discover the hidden issues which made big data complex. Major keywords identified include SOCIAL NETWORK ANALYSIS, HADOOP, MAPREDUCE, PERSONAL INFORMATION POLICY/PROTECTION/PRIVATE INFORMATION, CLOUD COMPUTING, VISUALIZATION, and DATA MINING. We finally suggest missing research themes to make big data a sustainable management innovation and convergence medium.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.2
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• pp.51-58
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• 2022

Blockchain technology is a system in which data from users participating in blockchain networks is distributed and stored. Bitcoin and Ethereum are attracting global attention, and the utilization of blockchain is expected to be endless. However, the need for blockchain data privacy protection is emerging in various financial, medical, and real estate sectors that process personal information due to the transparency of disclosing all data in the blockchain to network participants. Although studies using smart contracts, homomorphic encryption, and cryptographic key methods have been mainly conducted to protect existing blockchain data privacy, this paper proposes data privacy using matrix character relocation techniques differentiated from existing papers. The approach proposed in this paper consists largely of two methods: how to relocate the original data to matrix characters, how to return the deployed data to the original. Through qualitative experiments, we evaluate the safety of the approach proposed in this paper, and demonstrate that matrix character relocation will be sufficiently applicable in private blockchain environments by measuring the time it takes to revert applied data to original data.