• Journal of Information Processing Systems
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• v.15 no.6
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• pp.1265-1276
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• 2019

Data deduplication is a common method to improve cloud storage efficiency and save network communication bandwidth, but it also brings a series of problems such as privacy disclosure and dictionary attacks. This paper proposes a secure deduplication scheme for cloud storage based on Bloom filter, and dynamically extends the standard Bloom filter. A public dynamic Bloom filter array (PDBFA) is constructed, which improves the efficiency of ownership proof, realizes the fast detection of duplicate data blocks and reduces the false positive rate of the system. In addition, in the process of file encryption and upload, the convergent key is encrypted twice, which can effectively prevent violent dictionary attacks. The experimental results show that the PDBFA scheme has the characteristics of low computational overhead and low false positive rate.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.47-49
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• 2021

According to the revision of the Data 3 Act in 2020, personal information of medical data can be processed anonymously for statistical purposes, research, and public interest record keeping. However, unidentified data can be re-identified using genetic information, credit information, etc., and personal health information can be abused as sensitive information. In this paper, we derive the need for homomorphic encryption to protect the privacy of personal information separated by sensitive information.

• Journal of KIISE:Information Networking
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• v.36 no.2
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• pp.108-117
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• 2009

Data aggregation is one of the well-known techniques to reduce the energy consumption for information transmission over wireless sensor networks (WSN). As the WSNs are deployed in untrusted or even hostile environments, the data aggregation becomes problematic when end-to-end data privacy including data confidentiality and integrity between sensor nodes and base station, is required. Meanwhile, data homomorphic cryptoschemes have been investigated recently and recommended to provide the end-to-end privacy in the hostile environments. In order to assure both data confidentiality and integrity for data aggregation, this paper analyzes the existing homomorphic cryptoschemes and digital signature schemes, proposes possible combinations, and evaluates their performance in terms of CPU overheads and communication costs.

• Journal of Information Technology Applications and Management
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• v.29 no.3
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• pp.57-74
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• 2022

Recently, big data services have been used in various fields. In this situation, this research studied the intention to provide personal information from users, which is necessary to provide useful big data services. A survey was conducted on college students and ordinary people who have understood big data services. And path analysis was performed through Amos' structural equation. As a result of the study, it was found that privacy risks, trust in service providers, individual innovativeness, service incentives, social influence, and service design are major variables influencing the intention to provide personal information. And it was found that trust in service providers plays a mediating role in influencing the intention to provide personal information. In addition, big data services were classified into types for information acquisition and types related to purchase. Accordingly, it was further analyzed whether major variables differ in the path affecting the intention to provide personal information, and new implications were found. Companies that actually develop and provide big data services should establish different strategies by reflecting research results depending on the type of big data service provided.

• Korean Journal of Head & Neck Oncology
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• v.23 no.2
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• pp.147-152
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• 2007

Objectives and Background : Aims of this studies are to collect and analyze the lawful restriction against cancer registration and to suggest the model promoting the cancer registry. Materials and Methods : Total 16 countries, the members of OECD, including the U.S. are evaluated. the status of cancer registration of the evaluated countries are analyzed. The legislated laws, protect the individual's information, of the evaluated countries are analyzed. The cases any registries were impaired with the law to protect privacy are searched and analyzed. Results : All of the evaluated countries have some kinds of privacy protecting laws. For cancer registration, 11 of 16 countries implement some lawful authorities. Some of countries have experienced restriction of registration by the law of protecting individual's health data. All countries have performed cancer registry and 6 of 16 countries have nearly 100% population-based cancer registration. Conclusions : The cancer registry has to be the national effort. The informed consent of the data subjects and the permission of any special institutes are the difference to perform the registration. So, it is necessary to legislate any law supporting the cancer registration and establish any independent institutes to protect the individual's health data and support the cancer registry.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.12
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• pp.321-332
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• 2020

Decentralized approaches are extensively researched by academia and industry in order to cover up the flaws of existing systems in terms of data privacy. Blockchain and decentralized learning are prominent representatives of a deconcentrated approach. Blockchain is secure by design since the data record is irrevocable, tamper-resistant, consensus-based decision making, and inexpensive of overall transactions. On the other hand, decentralized learning empowers a number of devices collectively in improving a deep learning model without exposing the dataset publicly. To motivate participants to use their resources in building models, a decent and proportional incentive system is a necessity. A centralized incentive mechanism is likely inconvenient to be adopted in decentralized learning since it relies on the middleman that still suffers from bottleneck issues. Therefore, we design an incentive model for decentralized learning applications by leveraging the Ethereum smart contract. The simulation results satisfy the design goals. We also outline the concerns in implementing the presented scheme for sensitive data regarding privacy and data leakage.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.6
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• pp.157-167
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• 2023

The use of big data for transportation often involves using data that includes personal information, such as the driver's driving routes and coordinates. This study explores the creation of a route choice prediction model using a large dataset from mobile navigation apps using federated learning. This privacy-focused method used distributed computing and individual device usage. This study established preprocessing and analysis methods for driver data that can be used in route choice modeling and compared the performance and characteristics of widely used learning methods with federated learning methods. The performance of the model through federated learning did not show significantly superior results compared to previous models, but there was no substantial difference in the prediction accuracy. In conclusion, federated learning-based prediction models can be utilized appropriately in areas sensitive to privacy without requiring relatively high predictive accuracy, such as a driver's preferred route choice.

• Electronics and Telecommunications Trends
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• v.36 no.6
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• pp.1-12
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• 2021

As the demand for big data and big data-based artificial intelligence (AI) technology increases, the need for privacy preservations for sensitive information contained in big data and for high-speed encryption-based AI computation systems also increases. Fully homomorphic encryption (FHE) is a representative encryption technology that preserves the privacy of sensitive data. Therefore, FHE technology is being actively investigated primarily because, with FHE, decryption of the encrypted data is not required in the entire data flow. Data can be stored, transmitted, combined, and processed in an encrypted state. Moreover, FHE is based on an NP-hard problem (Lattice problem) that cannot be broken, even by a quantum computer, because of its high computational complexity and difficulty. FHE boasts a high-security level and therefore is receiving considerable attention as next-generation encryption technology. However, despite being able to process computations on encrypted data, the slow computation speed due to the high computational complexity of FHE technology is an obstacle to practical use. To address this problem, hardware technology that accelerates FHE operations is receiving extensive research attention. This article examines research trends associated with developments in hardware technology focused on accelerating the operations of representative FHE schemes. In addition, the detailed structures of hardware that accelerate the FHE operation are described.

• The Journal of Society for e-Business Studies
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• v.19 no.3
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• pp.65-89
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• 2014

Although many studies have focused on the influences and outcomes of personal information leakage, few studies have investigated how the personal information leakage prevention behavior differs depending on internet user. This study attempts to supplement the existing studies' limitations with the use of risk perception attitude (RPA) framework. More specifically, this study tries to show internet user can be classified into four groups based on perceived risk of personal information leakage and efficacy beliefs of personal information protection, and to identify how the groups differ in terms of motivation, information seeking, and behaviors for privacy leakage prevention. Analysis on survey data from 276 internet users reveals that the users can be classified into responsive, avoidance, proactive, indifference groups. Furthermore, there are differences between groups in terms of motivation, information seeking, and behaviors for personal information leakage prevention. This study contributes to expand existing literature by providing tailored guidelines for implementation of personal information protection strategies and policy.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.3
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• pp.625-633
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• 2018

Realm is an open source database developed to replace SQLite, which is commonly used in mobile devices. The data stored in the database must be checked during the digital forensic analysis process for mobile devices because it can help to understand the behavior of the user and whether the mobile device is operating or not. In addition, since the user can intentionally use anti-forensic techniques such as deleting data stored in the database, research on how to recover deleted records is needed. In this paper, we propose a method to recover records that have not been overwritten after deletion based on the analysis of the structure and record and deletion process of the Realm database file.