• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.6
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• pp.1373-1383
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• 2017

Deduplication is a function to effectively manage data and improve the efficiency of storage space. When the deduplication is applied to the system, it makes it possible to efficiently use the storage space by dividing the stored file into chunks and storing only unique chunk. However, the commercial digital forensic tool do not support the file system analysis, and the original file extracted by the tool can not be executed or opened. Therefore, in this paper, we analyze the process of generating chunks of data for a Windows Server 2012 system that can apply deduplication, and the structure of the resulting file(Chunk Storage). We also analyzed the case where chunks that are not covered in the previous study are compressed. Based on these results, we propose the method to collect deduplicated data and reconstruct the original file for digital forensic investigation.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.73-85
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• 2022

In the era of the 4th industrial revolution, where automation and connectivity are maximized with artificial intelligence, the importance of data collection and utilization for model update is increasing. In order to create a model using artificial intelligence technology, it is usually necessary to gather data in one place so that it can be updated, but this can infringe users' privacy. In this paper, we introduce federated learning, a distributed machine learning method that can update models in cooperation without directly sharing distributed stored data, and introduce a study to optimize distributed consensus among participants without an existing server. In addition, we propose a pattern and group-based distributed consensus optimization algorithm that uses an algorithm for generating patterns and groups based on the Kirkman Triple System, and performs parallel updates and communication. This algorithm guarantees more privacy than the existing distributed consensus optimization algorithm and reduces the communication time until the model converges.

• Annual Conference of KIPS
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• 2022.11a
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• pp.377-379
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• 2022

POI refers to the point of Interest in Location-Based Social Networks (LBSNs). With the rapid development of mobile devices, GPS, and the Web (web2.0 and 3.0), LBSNs have attracted many users to share their information, physical location (real-time location), and interesting places. The tremendous demand of the user in LBSNs leads the recommendation systems (RSs) to become more widespread attention. Recommendation systems assist users in discovering interesting local attractions or facilities and help social network service (SNS) providers based on user locations. Therefore, it plays a vital role in LBSNs, namely POI recommendation system. In the machine learning model, most of the training data are stored in the centralized data storage, so information that belongs to the user will store in the centralized storage, and users may face privacy issues. Moreover, sharing the information may have safety concerns because of uploading or sharing their real-time location with others through social network media. According to the privacy concern issue, the paper proposes a recommendation model to prevent user privacy and eliminate traditional RS problems such as cold-start and data sparsity.

• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.23-28
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• 2024

The multi-tenancy and high scalability of the cloud have inspired businesses and organizations across various sectors to adopt and deploy cloud computing. Cloud computing provides cost-effective, reliable, and convenient access to pooled resources, including storage, servers, and networking. Cloud service models, SaaS, PaaS, and IaaS, enable organizations, developers, and end users to access resources, develop and deploy applications, and provide access to pooled computing infrastructure. Despite the benefits, cloud service models are vulnerable to multiple security and privacy attacks and threats. The SaaS layer is on top of the PaaS, and the IaaS is the bottom layer of the model. The software is hosted by a platform offered as a service through an infrastructure provided by a cloud computing provider. The Hypertext Transfer Protocol (HTTP) delivers cloud-based apps through a web browser. The stateless nature of HTTP facilitates session hijacking and related attacks. The Open Web Applications Security Project identifies web apps' most critical security risks as SQL injections, cross-site scripting, sensitive data leakage, lack of functional access control, and broken authentication. The systematic literature review reveals that data security, application-level security, and authentication are the primary security threats in the SaaS model. The recommended solutions to enhance security in SaaS include Elliptic-curve cryptography and Identity-based encryption. Integration and security challenges in PaaS and IaaS can be effectively addressed using well-defined APIs, implementing Service Level Agreements (SLAs), and standard syntax for cloud provisioning.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.11
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• pp.5671-5693
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• 2017

With the rapid development of cloud computing, more and more data owners are motivated to outsource their data to cloud for various benefits. Due to serious privacy concerns, sensitive data should be encrypted before being outsourced to the cloud. However, this results that effective data utilization becomes a very challenging task, such as keyword search over ciphertexts. Although many searchable encryption methods have been proposed, they only support exact keyword search. Thus, misspelled keywords in the query will result in wrong or no matching. Very recently, a few methods extends the search capability to fuzzy keyword search. Some of them may result in inaccurate search results. The other methods need very large indexes which inevitably lead to low search efficiency. Additionally, the above fuzzy keyword search methods do not support access control. In our paper, we propose a searchable encryption method which achieves fuzzy search and access control through algorithm design and Ciphertext-Policy Attribute-based Encryption (CP-ABE). In our method, the index is small and the search results are accurate. We present word pattern which can be used to balance the search efficiency and privacy. Finally, we conduct extensive experiments and analyze the security of the proposed method.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.6
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• pp.1483-1490
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• 2017

Wearable devices need a host device to be paired with because of connectivity, functionality and ease personalization. There should be frequent update and backup processes between the paired devices even without user's consciousness. Due to pairing process, user-specific data are copied from smartphone and transferred to paired smartwatch. We focus on what happens in smartwatch because of pairing process. We perform an experiment study by observing and extracting data from smartwatch under real world usage phases. With a survey of user awareness on smartwatch regarding security and privacy, moreover, we suggest risk assessment on smartwatch in five levels, particularly considering pairing process based on security and privacy.

• Korea Trade Review
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• v.45 no.3
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• pp.71-89
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• 2020

This study was carried out to identify the impact of EU GDPR on international trade amid the ongoing digital trade liberalization. To do this, we first looked at the current trend of digital trade liberalization, the role of data in it, and the trade-restrictive elements of EU GDPR. This allowed us to identify the negative impact of GDPR on free trade. It then conducted an interview survey on Korean companies operating in the EU to verify the conclusions reached. The result of this survey showed that the level of GDPR risk perceived by Korean firms was very low compared with those of American, Japanese and Chinese firms. In particular, the impact of GDPR is not clear for Korea's SMEs. It can be assumed that the reason for this is that Korean SMEs are not using data as a major business tool while the capability of SMEs is sufficient to cope with GDPR. In this regard, the government's appropriate policies and further research for SMEs are needed.

• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.61-70
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• 2024

Cloud Computing is internet-based computing, where the users are provided with whatever service they need from the resources, software, and information. Recently, the security of cloud computing is considered as one of the major issues for both cloud service providers CSP and end-users. Privacy and highly confidential data make many users refuse to store their data within cloud computing, since data on cloud computing is not dully secured. The cryptographic algorithm is a technique which is used to maintain the security and privacy of the data on the cloud. In this research, we applied eight different cryptographic algorithms on Xen and KVM as hypervisors on cloud computing, to be able to measure and compare the performance of the two hypervisors. Response time and CPU utilization while encryption and decryption have been our aspects to measure the performance. In terms of response time and CPU utilization, results show that KVM is more efficient than Xen on average at 11.5% and 11% respectively. While TripleDES cryptographic algorithm shows a more efficient time response at Xen hypervisor than KVM.

• Convergence Security Journal
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• v.23 no.4
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• pp.3-15
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• 2023

As remote services and remote work become commonplace, the use of the Metaverse has grown. This allows transactions like real estate and finance in virtual Second Life. However, conducting economic activities in the Metaverse presents unique security challenges compared to the physical world and conventional cyberspace. To address these, the paper proposes solutions centered on authentication and privacy. It suggests improving data preprocessing based on Metaverse data's uniqueness and introduces a new authentication service using NFTs while adhering to W3C's DID framework. The system is implemented using Hyperledger Indy blockchain, and its success is confirmed through implementation analysis.

• The Journal of Bigdata
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• v.3 no.2
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• pp.1-10
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• 2018

In the field of smart water management, there is an increasing demand for strengthening competitiveness through big data analysis. As a result, systematic management (Governance) of big data is becoming an important issue. Big data governance is a systematic approach to evaluating, directing and monitoring data management, such as data quality assurance, privacy protection, data lifetime management, data ownership and clarification of management rights. Failure to establish big data governance can lead to serious problems by using low quality data for critical decisions. In addition, personal privacy data can make Big Brother worry come true, and IT costs can skyrocket due to the neglect of data age management. Even if these technical problems are fixed, the big data effects will not be sustained unless there are organizations and personnel who are dedicated and responsible for data-related issues. In this paper, we propose a method of building data governance for smart water data management based on big data.