• Information Systems Review
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• v.20 no.1
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• pp.17-39
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• 2018

SNS enables people to easily connect and communicate with each other. People share information, including personal information, through SNS. Users are concerned about their privacies, but they unconsciously or consciously disclose their personal information on SNS to interact with others. The privacy of a self-disclosed person can be intruded by others. A person can write, fabricate, or distribute a story using the disclosed information of another even without obtaining consent from the information owner. Many studies focused on privacy intrusion, especially from the perspective of a victim. However, only a few studies examined privacy intrusion from the perspective of an intruder on SNS. This study focuses on the intention of privacy intrusion from the perspective of an intruder on SNS and the factors that affect intention. Privacy intrusion intentions are categorized into two types. The first type is intrusion of privacy by writing one's personal information without obtaining consent from the information owner;, whereas the other type pertains to intrusion of privacy by distributing one's personal information without obtaining consent from the information owner. A research model is developed based on motivation theory to identify how these factors affect these two types of privacy intrusion intentions on SNS. From the perspective of motivation theory, we draw one extrinsic motivational factor (response cost) and four intrinsic motivational factors, namely, perceived enjoyment, experience of being intruded on privacy, experience of invading someone's privacy, and punishment behavior. After analyzing 202survey data, we conclude that different factors affect these two types of privacy intrusion intention. However, no relationship was found between the two types of privacy intrusion intentions. One of the most interesting findings is that the experience of privacy intrusion is the most significant factor related to the two types of privacy intrusion intentions. The findings contribute to the literature on privacy by suggesting two types of privacy intrusion intentions on SNS and identifying their antecedents from the perspective of an intruder. Practitioners can also use the findings to develop SNS applications that can improve protection of user privacies and legitimize proper regulations relevant to online privacy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.9
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• pp.2550-2572
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• 2023

Epidemiological survey is an important means for the prevention and control of infectious diseases. Due to the particularity of the epidemic survey, 1) epidemiological survey in epidemic prevention and control has a wide range of people involved, a large number of data collected, strong requirements for information disclosure and high timeliness of data processing; 2) the epidemiological survey data need to be disclosed at different institutions and the use of data has different permission requirements. As a result, it easily causes personal privacy disclosure. Therefore, traditional access control technologies are unsuitable for the privacy protection of epidemiological survey data. In view of these situations, we propose a black box-assisted fine-grained hierarchical access control scheme for epidemiological survey data. Firstly, a black box-assisted multi-attribute authority management mechanism without a trusted center is established to avoid authority deception. Meanwhile, the establishment of a master key-free system not only reduces the storage load but also prevents the risk of master key disclosure. Secondly, a sensitivity classification method is proposed according to the confidentiality degree of the institution to which the data belong and the importance of the data properties to set fine-grained access permission. Thirdly, a hierarchical authorization algorithm combined with data sensitivity and hierarchical attribute-based encryption (ABE) technology is proposed to achieve hierarchical access control of epidemiological survey data. Efficiency analysis and experiments show that the scheme meets the security requirements of privacy protection and key management in epidemiological survey.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.5
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• pp.1001-1009
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• 2015

In Smart Grid, smart meter sends our electric signal data to the main server of power supply in real-time. However, the more efficient the management of power loads become, the more likely the user's pattern of usage leaks. This paper points out the threat of privacy and the need of security measures in smart device environment by showing that it's possible to identify the appliances and the specific usage patterns of users from the smart meter's data. Learning algorithm PCA is used to reduce the dimension of the feature space and k-NN Classifier to infer appliances and states of them. Accuracy is validated with 10-fold Cross Validation.

• Journal of Computing Science and Engineering
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• v.5 no.3
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• pp.197-209
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• 2011

The increasing popularity of graph data, such as social and online communities, has initiated a prolific research area in knowledge discovery and data mining. As more real-world graphs are released publicly, there is growing concern about privacy breaching for the entities involved. An adversary may reveal identities of individuals in a published graph, with the topological structure and/or basic graph properties as background knowledge. Many previous studies addressing such attacks as identity disclosure, however, concentrate on preserving privacy in simple graph data only. In this paper, we consider the identity disclosure problem in weighted graphs. The motivation is that, a weighted graph can introduce much more unique information than its simple version, which makes the disclosure easier. We first formalize a general anonymization model to deal with weight-based attacks. Then two concrete attacks are discussed based on weight properties of a graph, including the sum and the set of adjacent weights for each vertex. We also propose a complete solution for the weight anonymization problem to prevent a graph from both attacks. In addition, we also investigate the impact of the proposed methods on community detection, a very popular application in the graph mining field. Our approaches are efficient and practical, and have been validated by extensive experiments on both synthetic and real-world datasets.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.4
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• pp.298-302
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• 2011

In this paper, we propose an efficient encryption algorithm for ensuring data privacy and security for cloud computing in mobile environments. As part of the evaluation of the proposed algorithm, we have implemented the algorithm in a PC environment and compared with the well-known encryption algorithm of the Data Encryption Standard (DES). The conventional DES algorithm is hard to maintain privacy, due to the fact that its initial and final permutation are known to the network To prevent this critical weakness, a triple DES algorithm has been reported, but it has a disadvantage of long encryption time. In this study, we propose random interleaving algorithm that uses the permutation table for improving privacy further. The proposed algorithm is found to run faster than the triple DES algorithm and also offers improved security in a wireless communication system.

• Annual Conference of KIPS
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• 2013.05a
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• pp.695-697
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• 2013

Medical data sharing is increasing due to treatment duplication which increases the cost of medication. Medical healthcare system has been improved to combine with cloud computing. It reduces treatment delay and the medical data error. However, the concern about the privacy protection of medical information is also significant. Medical information is more sensitive than other information because involuntary disclosure can affect in both personal and social life. Privacy cloud brokerage has conquered great attention for solving these problems. Our method provides a security model in the cloud computing environment that facilitates the exchange of medical records between assigned custodians. It allows doctors to obtain a complete patient medical records which can help to avoid duplication, reduce the medical error and healthcare cost as well. In addition, our method offers a trustworthy solution against the privacy violence.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.2
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• pp.391-400
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• 2012

This study focused on the role of the center for private information, which can manage and share the personal data from data breach incidents. Especially, this study addresses on the importance of establishing information management systems for preventing secondary misappropriation of breached personal data and private information. The database of breached personal data can be used for reducing privacy worries of potential victims of secondary misuse of personal data. Individuals who use the same IDs and passwords on multiple websites may find this service more effective and necessary. The effectiveness of this breached data center on reducing secondary privacy infringement may differ depending on the extend of data being shared and the conditions of data submission. When businesses experienced data breach and submission of data to this center is required by the law, the accuracy and effectiveness of this service can be enhanced. In addition, centralized database with high quality data set can increase matching for private information and control the secondary misappropriation of personal data or private information better.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.6
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• pp.1079-1090
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• 2014

Data mining is a technique to get the useful information that can be utilized for marketing and pattern analysis by processing the data that we have. However, when we use this technique, data provider's personal data can be leaked by accident. To protect these data from leakage, there were several techniques have been studied to preserve privacy. Vertically partitioned data is a state called that the data is separately provided to various number of user. On these vertically partitioned data, there was some methods developed to distinguishing kth element and (k+1) th element by using score. However, in previous method, we can only use on two-party case, so in this paper, we propose the extended technique by using paillier cryptosystem which can use on multi-party case.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.6057-6078
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• 2018

Smart home systems provide a safe, comfortable, and convenient living environment for users, whereby users enjoy featured home services supported by the data collected and generated by smart devices in smart home systems. However, existing smart devices lack sufficient protection in terms of data security and privacy, and challenging security and privacy issues inevitably emerge when using these data. This article aims to address these challenging issues by proposing a private blockchain-based access control (PBAC) scheme. PBAC involves employing a private blockchain to provide an unforgeable and auditable foundation for smart home systems, that can thwart illegal data access, and ensure the accuracy, integrity, and timeliness of access records. A detailed security analysis shows that PBAC could preserve data security against various attacks. In addition, we conduct a comprehensive performance evaluation to demonstrate that PBAC is feasible and efficient.

• Journal of KIISE
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• v.42 no.7
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• pp.919-928
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• 2015

In recent years, many research efforts have been made on privacy-preserving data mining (PPDM) in data of large volume. In this paper, we propose a PPDM solution based on principal component analysis (PCA), which can be widely used in computing correlation among sensitive data sets. The general method of computing PCA is to collect all the data spread in multiple nodes into a single node before starting the PCA computation; however, this approach discloses sensitive data of individual nodes, involves a large amount of computation, and incurs large communication overheads. To solve the problem, in this paper, we present an efficient method that securely computes PCA without the need to collect all the data. The proposed method shares only limited information among individual nodes, but obtains the same result as that of the original PCA. In addition, we present a dimensionality reduction technique for the proposed method and use it to improve the performance of secure similar document detection. Finally, through various experiments, we show that the proposed method effectively and efficiently works in a large amount of multi-dimensional data.