• Journal of the Korea Institute of Information Security & Cryptology
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• v.16 no.2
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• pp.3-12
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• 2006

The spread of mobile devices, PDAs and sensors has enabled the construction of ubiquitous computing environments, transforming regular physical spaces into 'smart space' augmented with intelligence and enhanced with services. However, unless privacy concerns are taken into account early in the design process of various ubiquitous devices(e.g. mobile devices, PDAs, sensors, etc.). we will end up crating ubiquitous surveillance infrastructure. Also, it may inappropriate to use public key techniques for computational constrained devices in ubiquitous computing environment. In this paper, we propose efficient user authentication and ky agreement protocol not only to preserve anonymity for protecting personal privacy but also to be suitable for computational constrained devices in ubiquitous computing environments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.1
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• pp.145-164
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• 2023

Cloud computing offers a platform that is both adaptable and scalable, making it ideal for outsourcing data for sharing. Various organizations outsource their data on cloud storage servers for availing management and sharing services. When the organizations outsource the data, they lose direct control on the data. This raises the privacy and security concerns. Cryptographic encryption methods can secure the data from the intruders as well as cloud service providers. Data owners may also specify access control policies such that only the users, who satisfy the policies, can access the data. Attribute based access control techniques are more suitable for the cloud environment as they cover large number of users coming from various domains. Multi-authority attribute-based encryption (MA-ABE) technique is one of the propitious attribute based access control technique, which allows data owner to enforce access policies on encrypted data. The main aim of this paper is to comprehensively survey various state-of-the-art MA-ABE schemes to explore different features such as attribute and key management techniques, access policy structure and its expressiveness, revocation of access rights, policy updating techniques, privacy preservation techniques, fast decryption and computation outsourcing, proxy re-encryption etc. Moreover, the paper presents feature-wise comparison of all the pertinent schemes in the field. Finally, some research challenges and directions are summarized that need to be addressed in near future.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3826-3851
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• 2016

With traditional data storage solutions becoming too expensive and cumbersome to support Big Data processing, enterprises are now starting to outsource their data requirements to third parties, such as cloud service providers. However, this outsourced initiative introduces a number of security and privacy concerns. In this paper, homomorphic encryption is suggested as a mechanism to protect the confidentiality and privacy of outsourced data, while at the same time allowing third parties to perform computation on encrypted data. This paper also discusses the challenges of Big Data processing protection and highlights its differences from traditional data protection. Existing works on homomorphic encryption are technically reviewed and compared in terms of their encryption scheme, homomorphism classification, algorithm design, noise management, and security assumption. Finally, this paper discusses the current implementation, challenges, and future direction towards a practical homomorphic encryption scheme for securing outsourced Big Data computation.

• 한국경영정보학회:학술대회논문집
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• 2007.06a
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• pp.388-394
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• 2007

정보기술(IT) 산업의 발달은 인터넷 활용을 급속도로 증가시켰으며, 인터넷 사용자의 수가 기하급수적으로 늘어감에 따라 인터넷을 상업적으로 활용하려는 욕구 또한 커져 가고 있다. 아울러 인터넷 관련 정보 기술의 발전속도가 매우 빠르게 발전되고 있다. 그러나 그 이면에는 개인정보의 무단유출, 사생활 노출 등의 역기능도 급격히 증가하고 있다. 이로 인해 오늘날 정보 유출로 인한 개인적 피해는 사회 문제로 대두되고 있으며, 개인의 프라이버시 침해에 대한 우려는 인터넷과 전자상거래 발달에 가장 큰 장애중의 하나로 대두되게 되었다. 온라인에서 비즈니스를 행하는 기업들은 이러한 사용자들의 프라이버시 침해에 대한 우려를 줄이고자 하는 다양한 노력들을 행하고 있다. 우선 프라이버시 보호에 관한 정책이나 규약을 홈페이지에 명시함으로써 사용자들을 안심시키려 하고 있으며, 동시에 금전적 인센티브나 편의를 제공함으로써 사용자들의 참여를 증가시키고 자신들의 비즈니스를 활성화시키고자 한다. 이에 본 연구에서는 기업의 어떠한 전략들이 사용자의 프라이버시 침해 우려를 낮추고 사용자들의 참여를 활성화시키는지를 동기부여의 기대이론에 근거하여 분석하고자 한다. 아울러 기업뿐만 아니라 사회적으로도 도움이 될 수 있는 프라이버시에 관한 전략들을 제안하고자 한다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.1
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• pp.97-106
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• 2014

CCTV(Closed Circuit television) is one of the widely used physical security technologies and video acquisition device installed at specific point with various purposes. Recently, as the CCTV capabilities improve, facial recognition from the information collected from CCTV video is under development. However, in case these technologies are exploited, concerns on major privacy infringement are high. Especially, a computer connected to a particular space images taken by the camera in real time over the Internet has emerged to show information services. In the privacy law, safety measures which is related with biometric template are notified. Accordingly, in this paper, for the protection of privacy video information in the video surveillance system, the technical and managerial requirements for video information security are suggested.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.3
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• pp.665-679
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• 2018

With the development of information and communication technology, especially wireless Internet technology and the spread of smart phones, digital data has increased. As a result, privacy issues which concerns about exposure of personal sensitive information are increasing. In this paper, we analyze the privacy vulnerability of online big data in domestic internet environment, especially focusing on portal service, and propose a measure to evaluate the possibility of privacy violation. For this purpose, we collected about 50 million user posts from the potal service contents and extracted the personal information. we find that potal service user can be identified by the extracted personal information even though the user id is partially anonymized. In addition, we proposed a risk measurement evaluation method that reflects the possibility of personal information linkage between service using partial anonymized ID and personal information exposure level.

• Journal of Digital Convergence
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• v.13 no.4
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• pp.219-225
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• 2015

There has been an exponential growth in the distribution and possession of sensitive information because of the emergence of various information channels such as smart devices, social media, etc. This enables the internet based web or mobile service operation institutions collecting the more personal information with ease, and in turn causes the issues of the privacy concerns. Followings are the results of this study: First, the information privacy concern has the negative effects upon the trust. Second, the information privacy concern has the negative effects upon the provision intention of personal information and the trust has positive effects upon the offering intention of personal information. At last, the offering intention of the personal information has the positive effects upon the behavior to provide the personal information.

• 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.

• Knowledge Management Research
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• v.21 no.4
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• pp.211-225
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• 2020

We investigate factors affecting chatbot use intention of online shopping mall users. We identify theoretical foundations from the literature and postulate that accuracy, personalization level, intelligence, intimacy, social presence, and piracy concern should affect intention to use more or negative intention to use. Based on 300 responses from online shopping mall chatbot users in Korea, we run the statistical analysis to assure the reliability and validity of the measurements. From the multiple regression analysis, we find that personalization level, intelligence, social presence, and privacy concerns significantly affect intention to use more. In contrast, we find that accuracy and privacy concerns significantly affect negative intention to use. This work will present pragmatic implications upon the design and management of chatbot in order to not only incent customers to use more but reduce factors that may cause negative use intention. Among functional factors, personalization and intelligence increases the intention to use more while accuracy decreases negative intention to use. Among emotional factors such as intimacy and social presence, we find that only social presence significantly increases intention to use more. Privacy concerns is found to decrease intention to use and increase negative intention to use.

• Informatization Policy
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• v.29 no.1
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• pp.38-59
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• 2022

The development of IT technology and the spread of smartphones are having a great impact on businesses and consumers. Consumers have been able to acquire more diverse and larger amounts of information than in the past due to information provided through smartphones and information search on the internet, which has a significant influence on decision-making. Companies have also become sensitive to such changes in consumer behavior patterns, reflected in their marketing. In addition, among the various characteristics of smartphones, location-based technology has become an important factor in providing targeted marketing from a company's point of view. Such technological development and social change have led to the expansion of the mobile advertising market, which promotes products or services based on the location of consumers and provides benefits such as discount coupons. In this study, we have analyzed the influence of consumer characteristics on intention to use in mobile location-based advertising, which has become an important marketing method in the mobile advertising market. The effects of variables of personalization, engagement, coupon proneness, economic efficiency, and irritation on attitude and information privacy concerns were analyzed, and the effects of attitude and information privacy concerns on intention to use were analyzed. The results of this study are deemed to be able to suggest factors to consider when providing mobile location-based advertising to consumers in the future.