• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.4
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• pp.545-558
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• 2021

Recently, Confidential computing plays an important role in next-generation cloud technology along with the development of trusted execution environments(TEEs), as it guarantees the trustworthiness of applications despite of untrusted nature of the cloud. Both academia and industry have actively proposed commercialized confidential computing solutions based on Intel SGX technology. However, the lack of clear criteria makes developers difficult to select a proper confidential computing framework among the possible options when implementing TEE-based cloud applications. In this paper, we derive baseline metrics that help to clarify the pros and cons of each framework through in-depth comparative analysis against existing confidential computing frameworks. Based on the comparison, we propose criteria to application developers for effectively selecting an appropriate confidential computing framework according to the design purpose of TEE-based applications.

• Electronics and Telecommunications Trends
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• v.34 no.4
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• pp.117-128
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• 2019

As companies use increasing amounts of data more and more, people are more concerned about protecting their privacy. Many researches studies have been conducted with a to securely view of manage managing and share sharing private information securely using the Bblockchain technology. These studies have suggested a Bblockchain-based approaches to provide efficiency, scalability, data ownership, and systematic data lifecycles that were are the limitations of lacking in traditional access controls. More Sspecifically, these studies have introduced a new access control models, distributed hash tables, trusted execution environments, and hierarchical ID-based cryptographic mechanisms to provide reliable access control even in complex environments such as IoT Internet of Things. In this paperstudy, we present the criteria to for classifying the functional characteristics of the Bblockchain-based access control methods and derive the differentiateion between of each the several methods.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.4
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• pp.7-18
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• 2017

Electrical energy is one of the most important energy sources in modern society. Therefore, it is very important to control the supply and demand of electric power. However, the power consumption data needed to predict power demand may include the information about the private behavior of an individual, the analysis of which may raise privacy issues. In this paper, we propose a secure power demand forecasting method where regression analyses on power consumption data are conducted in a trusted execution environment provided by Intel SGX, keeping the power usage pattern of users private. We performed experiments using various regression equations and selected an equation which has the least error rate. We show that the average error rate of the proposed method is lower than those of the previous forecasting methods with privacy protection functionality.

• Journal of Information Processing Systems
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• v.3 no.2
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• pp.43-53
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• 2007

The use of agent paradigm in today's applications is hampered by the security concerns of agents and hosts alike. The agents require the presence of a secure and trusted execution environment; while hosts aim at preventing the execution of potentially malicious code. In general, hosts support the migration of agents through the provision of an agent server and managing the activities of arriving agents on the host. Numerous studies have been conducted to address the security concerns present in the mobile agent paradigm with a strong focus on the theoretical aspect of the problem. Various proposals in Intrusion Detection Systems aim at securing hosts in traditional client-server execution environments. The use of such proposals to address the security of agent hosts is not desirable since migrating agents typically execute on hosts as a separate thread of the agent server process. Agent servers are open to the execution of virtually any migrating agent; thus the intent or tasks of such agents cannot be known a priori. It is also conceivable that migrating agents may wish to hide their intentions from agent servers. In light of these observations, this work attempts to bridge the gap from theory to practice by analyzing the security mechanisms available in Aglet. We lay the foundation for implementation of application specific protocols dotted with access control, secured communication and ability to detect tampering of agent data. As agents exists in a distributed environment, our proposal also introduces a novel security framework to address the security concerns of hosts through collaboration and pattern matching even in the presence of differing views of the system. The introduced framework has been implemented on the Aglet platform and evaluated in terms of accuracy, false positive, and false negative rates along with its performance strain on the system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.10
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• pp.5153-5170
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• 2016

The benefit of the scalability and flexibility inherent in cloud computing motivates clients to upload data and computation to public cloud servers. Because data is placed on public clouds, which are very likely to reside outside of the trusted domain of clients, this strategy introduces concerns regarding the security of sensitive client data. Thus, to provide sufficient security for the data stored in the cloud, it is essential to encrypt sensitive data before the data are uploaded onto cloud servers. Although data encryption is considered the most effective solution for protecting sensitive data from unauthorized users, it imposes a significant amount of overhead during the query processing phase, due to the limitations of directly executing operations against encrypted data. Recently, substantial research work that addresses the execution of SQL queries against encrypted data has been conducted. However, there has been little research on top-k join query processing over encrypted data within the cloud computing environments. In this paper, we develop an efficient algorithm that processes a top-k join query against encrypted cloud data. The proposed top-k join processing algorithm is, at an early phase, able to prune unpromising data sets which are guaranteed not to produce top-k highest scores. The experiment results show that the proposed approach provides significant performance gains over the naive solution.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.929-938
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• 2023

ARM processors, utilized in mobile devices, have integrated the hardware isolation framework, TrustZone technology, to implement two execution environments: the trusted domain "Secure World" and the untrusted domain "Normal World". Rootkit is a type of malicious software that gains administrative access and hide its presence to create backdoors. Detecting the presence of a rootkit in a Secure World is difficult since processes running within the Secure World have no memory access restrictions and are isolated. This paper proposes a technique that leverages the hardware based PMU(Performance Monitoring Unit) to measure events of the Secure World rootkit and to detect the rootkit using deep learning.