• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.3 no.6
• /
• pp.628-646
• /
• 2009

Users want to browse and search various web contents with mobile devices which can be used anywhere and anytime without limitations, in the same manner as desktop. But mobile devices have limited resources compared to desktop in terms of computing performance, network bandwidth, screen size for full browsing, and etc, so there are many difficulties in providing support for mobile devices to fully use desktop-based web contents. Recently, mobile network bandwidth has been greatly improved, however, since mobile devices cannot provide the same environment as desktop, users still feel inconvenienced. To provide web contents optimized for each user device, there have been studies about analyzing code to extract blocks for adaptation to a mobile environment. But since web contents are divided into several items such as menu, login, news, shopping, etc, if the block dividing basis is limited only to code or segment size, it will be difficult for users to recognize and find the items they need. Also it is necessary to resolve interface issues, which are the biggest inconvenience for users browsing in a mobile environment. In this paper, we suggest a personalized adaptation system that extracts item blocks from desktop-based web contents based on user interests, layers them, and adapts them for users so they can see preferred contents first.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.7 no.5
• /
• pp.1313-1327
• /
• 2013

How to make people keep both the confidentiality of the sensitive data and the privacy of their real identity in communication networks has been a hot topic in recent years. Researchers proposed privacy-preserving authenticated key exchange protocols (PPAKE) to answer this question. However, lots of PPAKE protocols need users to remember long secrets which are inconvenient for them. In this paper we propose a lightweight three-party privacy-preserving authentication key exchange (3PPAKE) protocol using smart card to address the problem. The advantages of the new 3PPAKE protocol are: 1. The only secrets that the users need to remember in the authentication are their short passwords; 2. Both of the users can negotiate a common key and keep their identity privacy, i.e., providing anonymity for both users in the communication; 3. It enjoys better performance in terms of computation cost and security. The security of the scheme is given in the random oracle model. To the best of our knowledge, the new protocol is the first provably secure authentication protocol which provides anonymity for both users in the three-party setting.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.4
• /
• pp.1489-1503
• /
• 2018

Spectrum reuse has attracted much concern of researchers and scientists, however, the dynamic spectrum access is challenging, since an individual secondary user usually just has limited sensing abilities. One key insight is that spectrum usage forecasting among secondary users, this inspiration enables users to obtain more informed spectrum opportunities. Therefore, spectrum usage forecasting is vital to cognitive radio networks (CRNs). With this insight, a spectrum usage forecasting model for the occurrence of primary users prediction is derived in this paper. The proposed model is based on auto regressive enhanced primary user emergence reasoning (AR-PUER), which combines linear prediction and primary user emergence reasoning. Historical samples are selected to train the spectrum usage forecasting model in order to capture the current distinction pattern of primary users. The proposed scheme does not require the knowledge of signal or of noise power. To verify the performance of proposed spectrum usage forecasting model, we apply it to the data during the past two months, and then compare it with some other sensing techniques. The simulation results demonstrate that the spectrum usage forecasting model is effective and generates the most accurate prediction of primary users occasion in several cases.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.3
• /
• pp.1570-1594
• /
• 2017

Social network services have recently changed from environments for simply building connections among users to open platforms for generating and sharing various forms of information. Existing user reputation computation methods are inadequate for determining the trust in users on social media where explicit ratings are rare, because they determine the trust in users based on user profile, explicit relations, and explicit ratings. To solve this limitation of previous research, we propose a user reputation computation method suitable for the social media environment by incorporating implicit as well as explicit ratings. Reliable user reputation is estimated by identifying malicious information raters, modifying explicit ratings, and applying them to user reputation scores. The proposed method incorporates implicit ratings into user reputation estimation by differentiating positive and negative implicit ratings. Moreover, the method generates user reputation scores for individual categories to determine a given user's expertise, and incorporates the number of users who participated in rating to determine a given user's influence. This allows reputation scores to be generated also for users who have received no explicit ratings, and, thereby, is more suitable for social media. In addition, based on the user reputation scores, malicious information providers can be identified.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.12
• /
• pp.5820-5834
• /
• 2017

This paper investigates the channel selection problem with dynamic users and the asymmetric interference relation in distributed opportunistic spectrum access systems. Since users transmitting data are based on their traffic demands, they dynamically compete for the channel occupation. Moreover, the heterogeneous interference range leads to asymmetric interference relation. The dynamic users and asymmetric interference relation bring about new challenges such as dynamic random systems and poor fairness. In this article, we will focus on maximizing the tradeoff between the achievable utility and access cost of each user, formulate the channel selection problem as a directional graphical game and prove it as an exact potential game presenting at least one pure Nash equilibrium point. We show that the best NE point maximizes both the personal and system utility, and employ the stochastic learning approach algorithm for achieving the best NE point. Simulation results show that the algorithm converges, presents near-optimal performance and good fairness, and the directional graphical model improves the systems throughput performance in different asymmetric level systems.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.1
• /
• pp.109-134
• /
• 2018

Collaborative Filtering (CF) is widely used in recommendation field, which can be divided into rating-based CF and learning-to-rank based CF. Although many methods have been proposed based on these two kinds of CF, there still be room for improvement. Firstly, the data sparsity problem still remains a big challenge for CF algorithms. Secondly, the malicious rating given by some illegal users may affect the recommendation accuracy. Existing CF algorithms seldom took both of the two observations into consideration. In this paper, we propose a recommendation method based on listwise learning-to-rank by incorporating users' social information. By taking both ratings and order of items into consideration, the Plackett-Luce model is presented to find more accurate similar users. In order to alleviate the data sparsity problem, the improved matrix factorization model by integrating the influence of similar users is proposed to predict the rating. On the basis of exploring the trust relationship between users according to their social information, a listwise learning-to-rank algorithm is proposed to learn an optimal ranking model, which can output the recommendation list more consistent with the user preference. Comprehensive experiments conducted on two public real-world datasets show that our approach not only achieves high recommendation accuracy in relatively short runtime, but also is able to reduce the impact of malicious ratings.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.6 no.5
• /
• pp.1286-1302
• /
• 2012

In this paper, the resource allocation problem with proportional fairness rate in cognitive OFDM-based wireless network is studied. It aims to maximize the total system throughput subject to constraints that include total transmit power for secondary users, maximum tolerable interferences of primary users, bit error rate, and proportional fairness rate among secondary users. It is a nonlinear optimization problem, for which obtaining the optimal solution is known to be NP-hard. An efficient bio-inspired suboptimal algorithm called immune clonal optimization is proposed to solve the resource allocation problem in two steps. That is, subcarriers are firstly allocated to secondary users assuming equal power assignment and then the power allocation is performed with an improved immune clonal algorithm. Suitable immune operators such as matrix encoding and adaptive mutation are designed for resource allocation problem. Simulation results show that the proposed algorithm achieves near-optimal throughput and more satisfying proportional fairness rate among secondary users with lower computational complexity.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.8
• /
• pp.3258-3275
• /
• 2015

The rise in recent technology has changed the ways, in which people communicate with one another. Social networking services (SNSs) have become one of the most representative means. General SNSs allow users to create their own unique profiles, search for fellow members, share information, etc., while other SNSs have functions that cater to different needs of users. As a result, users of SNSs have begun to pick and choose different SNSs and concurrently use multiple SNSs in order to fulfill all their needs. This exploratory study examined which SNSs are used together and the characteristics that predict the use of multiple SNSs. In addition, it observed the differences between consumers' usage of multiple SNSs in different cultures. An online survey was administered to SNS users in the United States and Korea. The results of the study showed that the use of multiple SNSs is not yet prevalent in Korea, the country that represented a collectivistic culture. In addition, in the U.S., the highest number of users reported that they were active on at least three SNSs.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.8 no.5
• /
• pp.1532-1553
• /
• 2014

Radio spectrum is a precious resource and characterized by fixed allocation policy. However, a large portion of the allocated radio spectrum is underutilized. Conversely, the rapid development of ubiquitous wireless technologies increases the demand for radio spectrum. Cognitive Radio (CR) methodologies have been introduced as a promising approach in detecting the white spaces, allowing the unlicensed users to use the licensed spectrum thus realizing Dynamic Spectrum Access (DSA) in an effective manner. This paper proposes a generalized framework for DSA between the licensed (primary) and unlicensed (secondary) users based on Continuous Time Markov Chain (CTMC) model. We present a spectrum access scheme in the presence of sensing errors based on CTMC which aims to attain optimum spectrum access probabilities for the secondary users. The primary user occupancy is identified by spectrum sensing algorithms and the sensing errors are captured in the form of false alarm and mis-detection. Simulation results show the effectiveness of the proposed spectrum access scheme in terms of the throughput attained by the secondary users, throughput optimization using optimum access probabilities, probability of interference with increasing number of secondary users. The efficacy of the algorithm is analyzed for both imperfect spectrum sensing and perfect spectrum sensing.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.8
• /
• pp.2865-2878
• /
• 2021

As a current popular technology, the blockchain has a serious issue: the private key cannot be retrieved due to force majeure. Since the outcome of the blockchain-based Bitcoin, there have been many occurrences of the users who lost or forgot their private keys and could not retrieve their token wallets, and it may cause the permanent loss of their corresponding blockchain accounts, resulting in irreparable losses for the users. We propose a recoverable private key scheme for consortium blockchain based on the verifiable secret sharing which can enable the user's private key in the consortium blockchain to be securely recovered through a verifiable secret sharing method. In our secret sharing scheme, users use the biometric keys to encrypt shares, and the preset committer peers in the consortium blockchain act as the participants to store the users' private key shares. Due to the particularity of the biometric key, only the user can complete the correct secret recovery. Our comparisons with the existing mnemonic systems or the multi-signature schemes have shown that our scheme can allow users to recover their private keys without storing the passwords accurately. Hence, our scheme can improve the account security and recoverability of the data-sharing systems across physical and virtual platforms that use blockchain technology.