• Journal of Korean Library and Information Science Society
• /
• v.43 no.3
• /
• pp.353-384
• /
• 2012

This study was conducted on the observation that the filter bubble and privacy violation problems are related to the personalized services provided by libraries. This study discussed whether there is the possibility for invasion of privacy when libraries provide services utilizing state-of-the-art technology, such as location-based services, context aware services, RFID-based services, Cloud Services, and book recommendation services. In addition, this study discussed the following three aspects: whether or not users give up their right to privacy when they provide personal information for online services, whether or not there are discussions about users' privacy in domestic libraries, and what kind of risks the filter bubble problem can cause library users and what are possible solutions. This study represents early-stage research on library privacy in Korea, and can be used as basic data for privacy research.

• International Journal of Computer Science & Network Security
• /
• v.22 no.3
• /
• pp.364-374
• /
• 2022

Health information systems (HIS) are facing security challenges on data privacy and confidentiality. These challenges are based on centralized system architecture creating a target for malicious attacks. Blockchain technology has emerged as a trending technology with the potential to improve data security. Despite the effectiveness of this technology, still HIS are suffering from a lack of data privacy and confidentiality. This paper presents a blockchain-based data storage security architecture integrated with an e-Health care system to improve its security. The study employed a qualitative research method where data were collected using interviews and document analysis. Execute-order-validate Fabric's storage security architecture was implemented through private data collection, which is the combination of the actual private data stored in a private state, and a hash of that private data to guarantee data privacy. The key findings of this research show that data privacy and confidentiality are attained through a private data policy. Network peers are decentralized with blockchain only for hash storage to avoid storage challenges. Cost-effectiveness is achieved through data storage within a database of a Hyperledger Fabric. The overall performance of Fabric is higher than Ethereum. Ethereum's low performance is due to its execute-validate architecture which has high computation power with transaction inconsistencies. E-Health care system administrators should be trained and engaged with blockchain architectural designs for health data storage security. Health policymakers should be aware of blockchain technology and make use of the findings. The scientific contribution of this study is based on; cost-effectiveness of secured data storage, the use of hashes of network data stored in each node, and low energy consumption of Fabric leading to high performance.

• The Journal of Economics, Marketing and Management
• /
• v.10 no.6
• /
• pp.17-26
• /
• 2022

Purpose: This study analyzes advanced cases of overseas smart cities and examines policy implications related to the creation of smart cities in Korea. Research design, data, and methodology: Analysis standards were established through the analysis of best practices. Analysis criteria include Technology, Privacy, Security, and Governance. Results: In terms of technology, U-City construction experience and communication infrastructure are strengths. Korea's ICT technology is inferior to major countries. On the other hand, mobile communication, IoT, Internet, and public data are at the highest level. The privacy section created six principles: legality, purpose limitation, transparency, safety, control, and accountability. Security issues enable urban crime, disaster and catastrophe prediction and security through the establishment of an integrated platform. Governance issues are handled by the Smart Special Committee, which serves as policy advisory to the central government for legal system, standardization, and external cooperation in the district. Conclusions: Private technology improvement and participation are necessary for privacy and urban security. Citizens should participate in smart city governance.

• Annual Conference of KIPS
• /
• 2022.05a
• /
• pp.264-267
• /
• 2022

Recently, federated learning (FL) has increased prominence as a viable approach for enhancing user privacy and data security by allowing collaborative multi-party model learning without exchanging sensitive data. Despite this, most present FL systems still depend on a centralized aggregator to generate a global model by gathering all submitted models from users, which could expose user privacy and the risk of various threats from malicious users. To solve these issues, we suggested a safe FL framework that employs differential privacy to counter membership inference attacks during the collaborative FL model training process and empowers blockchain to replace the centralized aggregator server.

• Electronics and Telecommunications Trends
• /
• v.39 no.5
• /
• pp.40-48
• /
• 2024

Quantum computers can likely perform computations that are unattainable by classical computers, and they represent the next generation of computing technologies. Due to high costs and complex maintenance, direct ownership of quantum computers by individuals users is challenging. Future utilization is predicted to involve quantum computing servers performing delegated computations for clients lacking quantum capabilities, similar to the current utilization of supercomputing. This delegation model allows several users to benefit from quantum computing without requiring ownership, thereby providing innovation potential in various fields. Ensuring data privacy and computational integrity in this model is critical for ensuring the reliability of quantum cloud computing services. However, these requirements are difficult to achieve because classical security techniques cannot be directly applied to quantum computing. We review research on security protocols for the delegation of quantum computing with focus on data privacy and integrity verification. Our analysis covers the background of quantum computing, privacy-preserving quantum computational models, and recent research trends. Finally, we discuss challenges and future directions for secure quantum delegated computations, highlighting their importance for the commercialization and widespread adoption of quantum computing.

• Journal of Intelligence and Information Systems
• /
• v.29 no.4
• /
• pp.165-184
• /
• 2023

Recently, Deep learning analysis of unstructured text data using language models, such as Google's BERT and OpenAI's GPT has shown remarkable results in various applications. Most language models are used to learn generalized linguistic information from pre-training data and then update their weights for downstream tasks through a fine-tuning process. However, some concerns have been raised that privacy may be violated in the process of using these language models, i.e., data privacy may be violated when data owner provides large amounts of data to the model owner to perform fine-tuning of the language model. Conversely, when the model owner discloses the entire model to the data owner, the structure and weights of the model are disclosed, which may violate the privacy of the model. The concept of offsite tuning has been recently proposed to perform fine-tuning of language models while protecting privacy in such situations. But the study has a limitation that it does not provide a concrete way to apply the proposed methodology to text classification models. In this study, we propose a concrete method to apply offsite tuning with an additional classifier to protect the privacy of the model and data when performing multi-classification fine-tuning on Korean documents. To evaluate the performance of the proposed methodology, we conducted experiments on about 200,000 Korean documents from five major fields, ICT, electrical, electronic, mechanical, and medical, provided by AIHub, and found that the proposed plug-in model outperforms the zero-shot model and the offsite model in terms of classification accuracy.

• Annual Conference of KIPS
• /
• 2024.05a
• /
• pp.711-714
• /
• 2024

In the dynamic landscape of modern machine learning, Federated Learning (FL) has emerged as a compelling paradigm designed to enhance privacy by enabling participants to collaboratively train models without sharing their private data. Specifically, Distillation-based Federated Learning, like Federated Learning with Model Distillation (FedMD), Federated Gradient Encryption and Model Sharing (FedGEMS), and Differentially Secure Federated Learning (DS-FL), has arisen as a novel approach aimed at addressing Non-IID data challenges by leveraging Federated Learning. These methods refine the standard FL framework by distilling insights from public dataset predictions, securing data transmissions through gradient encryption, and applying differential privacy to mask individual contributions. Despite these innovations, our survey identifies persistent vulnerabilities, particularly concerning the susceptibility to logit inversion attacks where malicious actors could reconstruct private data from shared public predictions. This exploration reveals that even advanced Distillation-based Federated Learning systems harbor significant privacy risks, challenging the prevailing assumptions about their security and underscoring the need for continued advancements in secure Federated Learning methodologies.

• Journal of Internet Technology
• /
• v.22 no.6
• /
• pp.1287-1297
• /
• 2021

Data aggregation is the significant process in which the information is gathered and combines data to decrease the amount of data transmission in the WSN. The sensor devices are susceptible to node attacks and security issues such as data confidentiality and data privacy are extremely important. A novel technique called Ruzicka Index Regressive Homomorphic Ephemeral Key Benaloh Cryptography (RIRHEKBC) technique is introduced for enhancing the security of data aggregation and data privacy in WSN. By applying the Ruzicka Index Regressive Homomorphic Ephemeral Key Benaloh Cryptography, Ephemeral private and public keys are generated for each sensor node in the network. After the key generation, the sender node performs the encryption using the receiver public key and sends it to the data aggregator. After receiving the encrypted data, the receiver node uses the private key for decrypting the ciphertext. The key matching is performed during the data decryption using Ruzicka Indexive regression function. Once the key is matched, then the receiver collects the original data with higher security. The simulation result proves that the proposed RIRHEKBC technique increases the security of data aggregation and minimizes the packet drop, and delay than the state-of-the- art methods.

• Journal of KIISE
• /
• v.41 no.10
• /
• pp.799-809
• /
• 2014

In this paper, we address the problem of computing Pearson correlation coefficients and Spearman's rank correlation coefficients in a secure manner while data providers preserve privacy of their own data in distributed environment. For a data mining or data analysis in the distributed environment, data providers(data owners) need to share their original data with each other. However, the original data may often contain very sensitive information, and thus, data providers do not prefer to disclose their original data for preserving privacy. In this paper, we formally define the secure correlation computation, SCC in short, as the problem of computing correlation coefficients in the distributed computing environment while preserving the data privacy (i.e., not disclosing the sensitive data) of multiple data providers. We then present SCC solutions for Pearson and Spearman's correlation coefficients using secure scalar product. We show the correctness and secure property of the proposed solutions by presenting theorems and proving them formally. We also empirically show that the proposed solutions can be used for practical applications in the performance aspect.

• The Journal of Economics, Marketing and Management
• /
• v.12 no.5
• /
• pp.9-20
• /
• 2024

Purpose: This study aims to analyze the application of Industry 5.0 technologies to improve residential welfare, focusing on vulnerable groups such as the elderly and one-person households. Research design, data, and methodology: Through a literature review and SWOT analysis, it examines both the strengths and challenges of these technologies, which include AI, IoT, energy management solutions, and personalized systems. Results: The application of Industry 5.0 technologies in residential welfare offers opportunities for enhanced personalization, energy efficiency, and security, especially for vulnerable groups like the elderly and one-person households. However, challenges such as high costs, data privacy, infrastructure limitations, and technological inequality must be addressed to ensure equitable access and widespread adoption. Conclusions: The research identifies key areas for improvement, including data privacy, infrastructure limitations, and the need for equitable access to advanced housing solutions. By addressing these areas, the adoption of Industry 5.0 technologies can help create a more resilient, inclusive, and efficient residential welfare system for future generations.