Browsing by Author "Zhang, Lifang"
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- Heterogeneous Data Storage Management with Deduplication in Cloud Computing
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä(2019) Yan, Zheng; Zhang, Lifang; Ding, Wenxiu; Zheng, QinghuaCloud storage as one of the most important services of cloud computing helps cloud users break the bottleneck of restricted resources and expand their storage without upgrading their devices. In order to guarantee the security and privacy of cloud users, data are always outsourced in an encrypted form. However, encrypted data could incur much waste of cloud storage and complicate data sharing among authorized users. We are still facing challenges on encrypted data storage and management with deduplication. Traditional deduplication schemes always focus on specific application scenarios, in which the deduplication is completely controlled by either data owners or cloud servers. They cannot flexibly satisfy various demands of data owners according to the level of data sensitivity. In this paper, we propose a heterogeneous data storage management scheme, which flexibly offers both deduplication management and access control at the same time across multiple Cloud Service Providers (CSPs). We evaluate its performance with security analysis, comparison and implementation. The results show its security, effectiveness and efficiency towards potential practical usage. - Predict Pairwise Trust based on Machine Learning in Online Social Networks: A Survey
A2 Katsausartikkeli tieteellisessä aikakauslehdessä(2018-09-10) Liu, Shushu; Zhang, Lifang; Yan, ZhengTrust plays a crucial role in online social networks where users do not communicate or interact with each other in a direct face-to-face manner. Although many researchers have already conducted comprehensive studies on trust computing like trust evaluation, pairwise trust prediction is still relatively under explored especially with machine learning methods which can overcome the disadvantages of both linear predication and trust propagation. This survey aims to fill this gap and first provides an overview of state-of-the-art researches in pairwise trust prediction using machine learning techniques, especially in the context of social networking. Specifically, we present a workflow of trust prediction using machine learning and summarize current available trust-related datasets, classifiers and different metrics used to evaluate a trained classifier. Also, we review, compare, and contrast the literature for the purpose of identifying open issues and directing future research. - Privacy-Preserving Trust Management for Unwanted Traffic Control
Sähkötekniikan korkeakoulu | Master's thesis(2016-10-27) Zhang, LifangThe pervasive use of the Internet has caused an incredible growth of unwanted traffic, such as spam, malware and malicious intrusions. Unwanted traffic must be controlled because it intrudes user devices, occupies driver memory, irritates users and burdens the network. Controlling unwanted traffic based on trust and reputation mechanisms has invited significant and rigorous research in recent years. However, few of the existing solutions consider and preserve the privacy of Internet hosts that report suspicious attacks. They cannot fulfill legal requirements, and are therefore impractical. This thesis proposes a privacy-preserving trust management system for unwanted traffic control by applying a homomorphic cryptosystem. The proposed system protects privacy, which is proved to be one-way and semantically secure against chosen-plaintext (IND-CPA) attacks if the Computational Composite Residuosity Assumption (CCRA) holds. The system is implemented and its performance is extensively examined in terms of computation complexity, communication overhead and storage consumption. The result shows the effectiveness and practicality of our system to preserve the privacy of Internet hosts in the detection and control of unwanted traffic. Possible methods to improve the performance have also been discussed in the thesis. - A Survey on Network Security-Related Data Collection Technologies
A2 Katsausartikkeli tieteellisessä aikakauslehdessä(2018) Lin, Huaqing; Yan, Zheng; Chen, Yu; Zhang, LifangSecurity threats and economic loss caused by network attacks, intrusions and vulnerabilities have motivated intensive studies on network security. Normally, data collected in a network system can reflect or can be used to detect security threats. We define these data as network security-related data. Studying and analyzing security-related data can help detect network attacks and intrusions, thus making it possible to further measure the security level of the whole network system. Obviously, the first step in detecting network attacks and intrusions is to collect security-related data. However, in the context of big data and 5G, there exist a number of challenges in collecting these security-related data. In this paper, we first briefly introduce network security-related data, including its definition and characteristics, and the applications of network data collection. We then provide the requirements and objectives for security-related data collection and present a taxonomy of data collection technologies. Moreover, we review existing collection nodes, collection tools and collection mechanisms in terms of network data collection and analyze them based on the proposed requirements and objectives towards high quality security-related data collection. Finally, we discuss open research issues and conclude with suggestions for future research directions.