18 results on '"Wenjuan, Tang"'
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2. Stop Deceiving! An Effective Defense Scheme Against Voice Impersonation Attacks on Smart Devices
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Yaoxue Zhang, Wenbin Huang, Hongbo Jiang, Jun Luo, and Wenjuan Tang
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Scheme (programming language) ,Artificial neural network ,Computer Networks and Communications ,Computer science ,Speech recognition ,Impersonation attack ,Mixture model ,Voice communication ,Computer Science Applications ,Support vector machine ,Hardware and Architecture ,Signal Processing ,Auditory sense ,Automatic speech ,computer ,Information Systems ,computer.programming_language - Abstract
Both voice communication and automatic speech verification (ASV) over smart devices are vulnerable to voice impersonation (VI) attack, which is often launched via imitating a target’s voice characteristics to deceive human auditory sense or fool the ASV system. Researchers have designed a number of defense schemes yet without consideration of universality due to the lack of comprehensive datasets. In this paper, we propose a universal defense scheme based on the VI dataset collected from a famous TV show named “The Sound”. First, we deliver a thorough study on the VI attacks in both auditory and ASV systems to verify the collected simulated voice could spoof the auditory and the ASV system with a notable probability. Second, we propose a quasi-gaussian distribution (QGD) based defense scheme with the discovery about specific voice characteristics are distinct between attackers and targets. Finally, we conduct extensive experimental results on our collected VI dataset as well as the auxiliary ASVspoof2017 dataset, to indicate the proposed QGD scheme outperforms the state-of-the-art schemes: Back-Propagation Neural Network, Support Vector Machine and Gaussian Mixture Model, in terms of accuracy.
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- 2022
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3. Secure Data Sharing With Flexible User Access Privilege Update in Cloud-Assisted IoMT
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Huimei Wang, Jian Liu, Wenjuan Tang, Ming Xian, Jialu Hao, and Cheng Huang
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Revocation ,business.industry ,Computer science ,Cloud computing ,Privilege (computing) ,Computer security ,computer.software_genre ,Encryption ,Computer Science Applications ,Human-Computer Interaction ,Data sharing ,Ciphertext ,Computer Science (miscellaneous) ,The Internet ,Confidentiality ,business ,computer ,Information Systems - Abstract
Cloud-assisted Internet of Medical Things (IoMT) is becoming an emerging paradigm in the healthcare domain, which involves collection, storage and usage of the medical data. Considering the confidentiality and accessibility of the outsourced data, secure and fine-grained data sharing is a crucial requirement for the patients. Attribute-based encryption (ABE) is a promising solution to deal with this issue, but considering its property of each attribute sharing with multiple users, how to flexibly and efficiently update access privileges of certain users without affecting others is still a serious challenge. In this paper, we propose a secure and fine-grained data sharing scheme with flexible user access privilege update in cloud-assisted IoMT environment. Specifically, we take ABE as the basic building block, and utilize proxy re-encryption and key blinding techniques to empower the cloud server to re-encrypt the ciphertext affected by revocation and update keys for unrevoked users. In addition, adding attributes for users to extend their access rights is realized only based on few key components stored in cloud without entirely re-computing and re-issuing keys for them. As a result, the patients are able to flexibly and efficiently share their data and manage users' privileges. Formal proof and detailed performance evaluation demonstrate the security and efficiency of the proposed scheme.
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- 2022
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4. Efficient personalized search over encrypted data for mobile edge-assisted cloud storage
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Wenjuan Tang, Guojun Wang, Karim Alinani, Qiang Zhang, Xin Li, and Qin Liu
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Security analysis ,Cryptographic primitive ,Computer Networks and Communications ,business.industry ,Computer science ,020206 networking & telecommunications ,Cloud computing ,02 engineering and technology ,Encryption ,Personalized search ,Server ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Enhanced Data Rates for GSM Evolution ,business ,Cloud storage ,Computer network - Abstract
Cloud storage services allow a data owner to share her/his outsourced data with other users, and enable the users to search target data by keywords. To ensure the data confidentiality, data owner always encrypt data using traditional encryption schemes before outsourcing. Whereas, it makes efficiently searching impossible. Symmetric searchable encryption (SSE) is a cryptographic primitive that resolves this tension. However, most existing SSE schemes do not consider the individual characteristics of users during the search, such that they cannot support personalized search services over encrypted data. Meanwhile, security and efficiency issues in the cloud service model have also severely affected the user’s search experience, and the introduction of mobile edge servers can solve these problems to some extent. In this paper, we propose a personalized searchable encryption scheme (PSED) for mobile edge-assisted cloud storage. Our contribution consists of three aspects. First, we incorporate the user’s preference factors into the user’s query which enable users to get accurate personalized search results. Second, the computational overhead of the cloud server is reduced by calculating the relevance scores of the subqueries and subindexes on mobile edge servers. Third, by cutting the index and the query matrix, the encryption efficiency of the index and the query matrix is improved. Security analysis shows that PSED can guarantee the privacy of the data and the user. Experimental results demonstrate that the proposed schemes are highly efficient and accurate.
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- 2021
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5. A Deep Learning-Based Mobile Crowdsensing Scheme by Predicting Vehicle Mobility
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Md. Zakirul Alam Bhuiyan, Wenjuan Tang, Anfeng Liu, Xiaoyu Zhu, and Yueyi Luo
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Scheme (programming language) ,050210 logistics & transportation ,Computer science ,business.industry ,Mechanical Engineering ,Deep learning ,05 social sciences ,Real-time computing ,Computer Science Applications ,Set (abstract data type) ,Crowdsensing ,0502 economics and business ,Automotive Engineering ,Trajectory ,Task analysis ,Data center ,Artificial intelligence ,Online algorithm ,business ,computer ,computer.programming_language - Abstract
Mobile crowdsensing is an emerging paradigm that selects users to complete sensing tasks. Recently, mobile vehicles are adopted to perform sensing data collection tasks in the urban city due to their ubiquity and mobility. In this article, we study how mobile vehicles can be optimally selected in order to collect maximum data from the urban environment in a future period of tens of minutes. We formulate the recruitment of vehicles as a maximum data limited budget problem. The application scenario is generalized to a realistic online setting where vehicles are continuously moving in real-time and the data center decides to recruit a set of vehicles immediately. A deep learning-based scheme through mobile vehicles (DLMV) is proposed to collect sensing data in the urban environment. We first propose a deep learning-based offline algorithm to predict vehicle mobility in a future time period. Furthermore, we propose a greedy online algorithm to recruit a subset of vehicles with a limited budget for the NP-Complete problem. Extensive experimental evaluations are conducted on the real mobility dataset in Rome. The results have not only verified the efficiency of our proposed solution but also validated that DLMV can improve the quantity of collected sensing data compared with other algorithms.
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- 2021
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6. Privacy-preserving task recommendation with win-win incentives for mobile crowdsourcing
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Wenjuan Tang, Yaoxue Zhang, Kuan Zhang, Ju Ren, and Xuemin Shen
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Scheme (programming language) ,Service (systems architecture) ,Information Systems and Management ,Computer science ,02 engineering and technology ,Crowdsourcing ,Computer security ,computer.software_genre ,Encryption ,Theoretical Computer Science ,Task (project management) ,Artificial Intelligence ,0202 electrical engineering, electronic engineering, information engineering ,Overhead (computing) ,computer.programming_language ,business.industry ,05 social sciences ,050301 education ,Computer Science Applications ,Win-win game ,Incentive ,Control and Systems Engineering ,020201 artificial intelligence & image processing ,business ,0503 education ,computer ,Software - Abstract
Mobile crowdsourcing enables mobile requesters to publish tasks, which can be accomplished by workers with awards. However, existing task allocation schemes face tradeoff between effectiveness and privacy preservation, and most of them lack consideration of win-win incentives for both requesters and workers participation. In this paper, we propose a privacy-preserving task recommendation scheme with win-win incentives in crowdsourcing through developing advanced attribute-based encryption with preparation/online encryption and outsourced decryption technologies. Specifically, we design bipartite matching between published tasks and participant workers, to recommend tasks for eligible workers with interests and provide valuable task accomplishment for requesters in a win-win manner. Furthermore, our scheme reduces encryption cost for requesters by splitting encryption into preparation and online phases, as well as shifts most of the decryption overhead from the worker side to the service platform. Privacy analysis demonstrates requester and worker privacy preservation under chosen-keyword attack and chosen-plaintext attack. Performance evaluation shows cost-efficient computation overhead for requesters and workers.
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- 2020
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7. Secure Information Transmissions in Wireless-Powered Cognitive Radio Networks for Internet of Medical Things
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Weizhi Meng, Zhiyuan Tan, Kun Tang, Entao Luo, Wenjuan Tang, and Lianyong Qi
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Beamforming ,Science (General) ,Optimization problem ,Article Subject ,Computer Networks and Communications ,Computer science ,Internet of Medical Things (IoMT) ,Data_CODINGANDINFORMATIONTHEORY ,02 engineering and technology ,Cyber-security ,secrecy ,Q1-390 ,0203 mechanical engineering ,Secrecy ,Centre for Distributed Computing, Networking and Security ,0202 electrical engineering, electronic engineering, information engineering ,T1-995 ,Wireless ,Technology (General) ,business.industry ,Transmitter ,medical information ,020302 automobile design & engineering ,020206 networking & telecommunications ,AI and Technologies ,Cognitive radio ,Transmission (telecommunications) ,Convex optimization ,secure transmissions ,business ,Information Systems ,Computer network - Abstract
In this paper, we consider the issue of the secure transmissions for the cognitive radio-based Internet of Medical Things (IoMT) with wireless energy harvesting. In these systems, a primary transmitter (PT) will transmit its sensitive medical information to a primary receiver (PR) by a multi-antenna-based secondary transmitter (ST), where we consider that a potential eavesdropper may listen to the PT’s sensitive information. Meanwhile, the ST also transmits its own information concurrently by utilizing spectrum sharing. We aim to propose a novel scheme for jointly designing the optimal parameters, i.e., energy harvesting (EH) time ratio and secure beamforming vectors, for maximizing the primary secrecy transmission rate while guaranteeing secondary transmission requirement. For solving the nonconvex optimization problem, we transfer the problem into convex optimization form by adopting the semidefinite relaxation (SDR) method and Charnes–Cooper transformation technique. Then, the optimal secure beamforming vectors and energy harvesting duration can be obtained easily by utilizing the CVX tools. According to the simulation results of secrecy transmission rate, i.e., secrecy capacity, we can observe that the proposed protocol for the considered system model can effectively promote the primary secrecy transmission rate when compared with traditional zero-forcing (ZF) scheme, while ensuring the transmission rate of the secondary system.
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- 2020
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8. Efficient and Privacy-preserving Fog-assisted Health Data Sharing Scheme
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Yaoxue Zhang, Xuemin Shen, Kuan Zhang, Ju Ren, Wenjuan Tang, and Deyu Zhang
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Scheme (programming language) ,business.industry ,Computer science ,Node (networking) ,020206 networking & telecommunications ,Access control ,02 engineering and technology ,Energy consumption ,Encryption ,Computer security ,computer.software_genre ,Theoretical Computer Science ,Data sharing ,Artificial Intelligence ,Collusion ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Confidentiality ,business ,computer ,computer.programming_language - Abstract
Pervasive data collected from e-healthcare devices possess significant medical value through data sharing with professional healthcare service providers. However, health data sharing poses several security issues, such as access control and privacy leakage, as well as faces critical challenges to obtain efficient data analysis and services. In this article, we propose an efficient and privacy-preserving fog-assisted health data sharing (PFHDS) scheme for e-healthcare systems. Specifically, we integrate the fog node to classify the shared data into different categories according to disease risks for efficient health data analysis. Meanwhile, we design an enhanced attribute-based encryption method through combination of a personal access policy on patients and a professional access policy on the fog node for effective medical service provision. Furthermore, we achieve significant encryption consumption reduction for patients by offloading a portion of the computation and storage burden from patients to the fog node. Security discussions show that PFHDS realizes data confidentiality and fine-grained access control with collusion resistance. Performance evaluations demonstrate cost-efficient encryption computation, storage and energy consumption.
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- 2019
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9. Secure Data Aggregation of Lightweight E-Healthcare IoT Devices With Fair Incentives
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Wenjuan Tang, Kun Deng, Ju Ren, and Yaoxue Zhang
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021110 strategic, defence & security studies ,Information privacy ,Distributed database ,Computer Networks and Communications ,business.industry ,Computer science ,0211 other engineering and technologies ,020206 networking & telecommunications ,Cryptography ,02 engineering and technology ,Computer security ,computer.software_genre ,Secret sharing ,Computer Science Applications ,Data aggregator ,Hardware and Architecture ,Server ,Signal Processing ,Health care ,0202 electrical engineering, electronic engineering, information engineering ,Differential privacy ,business ,computer ,Information Systems - Abstract
With rapid development of e-healthcare systems, patients that are equipped with resource-limited e-healthcare devices (Internet of Things) generate huge amount of health data for health management. These health data possess significant medical value when aggregated from these distributed devices. However, efficient health data aggregation poses several security and privacy issues such as confidentiality disclosure and differential attacks, as well as patients may be reluctant to contribute their health data for aggregation. In this paper, we propose a privacy-preserving heath data aggregation scheme that securely collects health data from multiple sources and guarantee fair incentives for contributing patients. Specifically, we employ signature techniques to keep fair incentives for patients. Meanwhile, we add noises into the health data for differential privacy. Furthermore, we combine Boneh–Goh–Nissim cryptosystem and Shamir’s secret sharing to keep data obliviousness security and fault tolerance. Security and privacy discussions show that our scheme can resist differential attacks, tolerate healthcare centers failures, and keep fair incentives for patients. Performance evaluations demonstrate cost-efficient computation, communication and storage overhead.
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- 2019
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10. Fine-Grained TDMA MAC Design toward Ultra-Reliable Broadcast for Autonomous Driving
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Wenjuan Tang, Ju Ren, Yaoxue Zhang, Feng Lyu, Peng Yang, Nan Cheng, and Xuemin Sherman Shen
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Situation awareness ,Computer science ,business.industry ,ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS ,Time division multiple access ,020206 networking & telecommunications ,Topology (electrical circuits) ,02 engineering and technology ,Broadcasting ,Collision ,Computer Science Applications ,Broadcast communication network ,0202 electrical engineering, electronic engineering, information engineering ,Electrical and Electronic Engineering ,business ,Protocol (object-oriented programming) ,Collision avoidance ,Computer network - Abstract
In the autonomous driving era, V2X communication is essential since it enables rapid message dissemination via periodical beacon exchange (broadcast communication), which contributes to better situation awareness and maneuvering cooperation. However, designing a MAC protocol for reliable V2X broadcasting is challenging, as minimal beacon delivery delay and collision avoidance should be achieved simultaneously. In this article, we design a fine-grained TDMA-based MAC protocol to support ultra-reliable broadcast for autonomous vehicles. Specifically, three critical issues are first identified: mobility-caused time slot collision, time slot shortage, and stiff beacon rate limitation. Accordingly, three fine-grained solutions are provided to tackle those issues: mobility-aware time slot assignment, beacon rate adaption with safety awareness, and flexible beacon rates support. Moreover, a case study on mobility-aware time slot assignment based on road topology and lane distribution is presented, with simulation results' verification. Finally, we elaborate the steps to implement the fine-grained MAC protocol in autonomous driving environments.
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- 2019
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11. Fog-Enabled Smart Health: Toward Cooperative and Secure Healthcare Service Provision
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Deyu Zhang, Kuan Zhang, Ju Ren, Wenjuan Tang, Yaoxue Zhang, and Xuemin Sherman Shen
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Health risk assessment ,Computer Networks and Communications ,business.industry ,Computer science ,020206 networking & telecommunications ,02 engineering and technology ,Encryption ,Computer security ,computer.software_genre ,Computer Science Applications ,Information and Communications Technology ,Server ,Health care ,0202 electrical engineering, electronic engineering, information engineering ,Electrical and Electronic Engineering ,Healthcare service ,business ,computer ,Edge computing - Abstract
The rise of smart health promotes ubiquitous healthcare services with the adoption of information and communication technologies. However, increasing demands of medical services require more computing and storage resources in proximity of medical users for intelligent sensing, processing, and analysis. Fog computing emerges to enable in situ data processing and service provision for smart health in proximity of medical users, exploiting a large number of small-scale servers. In this article, we investigate fog-enabled smart health toward cooperative and secure healthcare service provision. Specifically, we first introduce the overall infrastructure and some promising applications, including emergent healthcare service, health risk assessment, and healthcare notification. We then discuss the challenges of fog-enabled smart health from the perspectives of cooperation and security. A case study is presented to demonstrate efficient and secure health data sharing through naive Bayes classification and attribute-based encryption with assistance from fog computing. Finally, by exploring interesting future directions, more attention can be attracted to this emerging area.
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- 2019
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12. Enabling Trusted and Privacy-Preserving Healthcare Services in Social Media Health Networks
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Yaoxue Zhang, Wenjuan Tang, and Ju Ren
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Social network ,Computer science ,business.industry ,Internet privacy ,Resistance (psychoanalysis) ,02 engineering and technology ,Computer Science Applications ,Server ,Signal Processing ,Health care ,0202 electrical engineering, electronic engineering, information engineering ,Media Technology ,Collaborative filtering ,Sybil attack ,Openness to experience ,ComputingMilieux_COMPUTERSANDSOCIETY ,020201 artificial intelligence & image processing ,The Internet ,Social media ,Electrical and Electronic Engineering ,business - Abstract
Social Media Health Networks provide a promising paradigm to attract patients to share and communicate their personal health status with other online patients, and consult healthcare services from online caregivers with social networks. Social Media Health Networks transform healthcare services from time-consuming offline hospital-centered paradigm to the convenient and efficient online paradigm through Internet, which can expand the traditional healthcare services and shorten the information gap between patients and caregivers. However, how to build the trust between patients and caregivers raises a challenging issue due to the openness of the social networks; meanwhile, the personal privacy may be disclosed when sharing personal health information with other patients and caregivers. In this paper, we propose a personalized and trusted healthcare service approach to enable trusted and privacy-preserving healthcare services in social media health networks, which can improve the trustiness between patients and caregivers through authentic ratings toward caregivers and guarantee the patients’ privacy. Specifically, we employ the collaborative filtering model to seek appropriate personalized caregivers, bloom filter to extract and map the personal healthcare symptoms, and inner product to compute the similarity between patients for finding patients with similar health symptoms in a privacy-preserving way. Meanwhile, to guarantee authentic ratings and reviews toward caregivers, we develop a sybil attack detection scheme to find patients’ fake ratings and reviews using different pseudonyms. Security analysis shows that our proposed approach can preserve the privacy of patients and prevent sybil attacks. Performance evaluation demonstrates that our approach can achieve prominent performance improvement, in terms of personalized caregivers finding and sybil attack resistance.
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- 2019
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13. A case for software-defined code scheduling based on transparent computing
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Di Zhang, Wenjuan Tang, Yuezhi Zhou, Xiang Lan, and Yaoxue Zhang
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020203 distributed computing ,Computer Networks and Communications ,Wireless network ,business.industry ,Computer science ,Distributed computing ,020206 networking & telecommunications ,Cloud computing ,02 engineering and technology ,Application software ,computer.software_genre ,Virtualization ,Software ,User experience design ,Utility computing ,0202 electrical engineering, electronic engineering, information engineering ,Code (cryptography) ,business ,computer - Abstract
Although cloud computing has made significant achievement, it still faces many challenges, such as bad interactive performance and unsatisfying user experience over a long-haul wide-area or wireless network. To address these challenges, we proposed a software-defined stream-based code scheduling framework according to the concept of transparent computing. This framework uses the idea of code streaming to decouple the computation and storage of software codes; this idea also leverages the input/output virtualization technique to support legacy operating systems and application software in a feasible and effective way. The software-defined code scheduling framework allows the computation or storage to be adaptively carried out at appropriate machines with the assistance of performance and capacity monitoring facilities. Thus, the framework can improve application performance and user experiences by executing software codes on a nearer or better machine. We developed a pilot system to investigate the advantages of the proposed framework. Preliminary experimental results show that our approach can achieve better performance than current cloud computing-based systems.
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- 2017
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14. A Game Theoretic D2D Local Caching System under Heterogeneous Video Preferences and Social Reciprocity
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Wenjuan Tang, Yuezhi Zhou, Yaoxue Zhang, Shuang Li, and Kaichuan Zhao
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Game theoretic ,business.industry ,Computer science ,Iterative method ,media_common.quotation_subject ,05 social sciences ,050801 communication & media studies ,020206 networking & telecommunications ,02 engineering and technology ,0508 media and communications ,Utility maximization problem ,0202 electrical engineering, electronic engineering, information engineering ,Cellular network ,Social relationship ,Stackelberg competition ,Quality (business) ,business ,Mobile device ,Computer network ,media_common - Abstract
To accommodate the increasing rich multimedia mobile traffics, especially the mobile video traffics, local caching becomes an effective approach to improve the quality of content delivering services in the cellular networks. Mobile devices with large storage capacities and high speed device-to-device (D2D) links become important elements of the local caching system. In this paper, we propose a D2D local caching system under heterogeneous preferences of mobile subscribers (MS), and investigate the utility maximization problem using Stackelberg game solution. In particular, the MSs form different groups, according to their social relationships, and determine the price policies to maximize their utilities, while the video provider (VP) aims to maximize his profits by deciding the rent policies and the budget plan. We investigate the equilibrium of the Stackelberg game in details and propose a water-filling based iterative algorithm to obtain the Stackelberg equilibrium. Extensive results demonstrate efficient performance of the D2D local caching system.
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- 2018
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15. Lightweight and Privacy-Preserving Fog-Assisted Information Sharing Scheme for Health Big Data
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Xuemin Shen, Wenjuan Tang, Kuan Zhang, Yaoxue Zhang, and Ju Ren
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Information privacy ,Computer science ,business.industry ,Information sharing ,Big data ,Volume (computing) ,020206 networking & telecommunications ,Cloud computing ,Access control ,02 engineering and technology ,Computer security ,computer.software_genre ,Encryption ,Server ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,business ,computer - Abstract
With the advancements of electronic medical equipment, e-healthcare system becomes a promising paradigm to continuously monitor health conditions and remotely diagnose phenomena. Meanwhile, it generates a large volume of health data and poses several security challenges, such as access control and privacy leakage. In this paper, we propose a lightweight and privacy- preserving fog-assisted information sharing scheme (PFHD) for health big data. Specifically, we integrate fog computing into e-healthcare system to pre-process the raw health data and improve the efficiency of health data analysis. Furthermore, to prevent privacy leakage, we design a hierarchical attribute-based encryption method by encrypting the profile and health data with different access policies. In addition, we reduce the computation cost on devices by offloading health data encryption from devices to fog servers. Security discussions show that PFHD can achieve fine- grained health data sharing with privacy preservation. Performance evaluations demonstrate the efficiency of PFHD, especially in terms of encryption computation and storage costs.
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- 2017
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16. Software-Defined Streaming-Based Code Scheduling for Transparent Computing
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Yaoxue Zhang, Wenjuan Tang, Yuezhi Zhou, and Di Zhang
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business.industry ,Wireless network ,Computer science ,Distributed computing ,020206 networking & telecommunications ,Cloud computing ,02 engineering and technology ,Application software ,computer.software_genre ,Virtualization ,Software ,User experience design ,Server ,0202 electrical engineering, electronic engineering, information engineering ,Code (cryptography) ,020201 artificial intelligence & image processing ,business ,computer - Abstract
Although cloud computing has made significant achievement, it still faces many challenges, such as slow interactive performance and unsatisfying user experience over a long-haul wide-area or wireless network. To address these challenges, we proposed a software-defined streaming-based code scheduling framework according to the concept of transparent computing. This framework uses the idea of code streaming to decouple the computation and storage of software codes, this idea also leverages the input/output virtualization technique to support legacy operating systems and application software in a feasible and effective way. The software-defined code scheduling framework allows the computation or storage to be adaptively carried out at appropriate machines with the assistance of performance and capacity monitoring facilities. Thus, the framework can improve application performance and user experiences by executing software codes on a nearer or better machine. We developed a pilot system to investigate the advantages of the proposed framework. Preliminary experimental results show that our approach can achieve a better performance than current cloud computing-based systems.
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- 2016
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17. Design and Implementation of Streaming Application Execution Platform in Android
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Wenjuan Tang, Binji Mo, Yang Xu, and Guojun Wang
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Java ,business.industry ,Computer science ,Streaming application ,computer.software_genre ,Open source ,User experience design ,Server ,Embedded system ,Operating system ,Transparent computing ,Android (operating system) ,business ,computer ,Humanoid robot ,computer.programming_language - Abstract
Because of benefit from the strategy of open source and the strong ability of innovation, Android has become one of the most popular operating systems on mobile platform. While the explosive increase of mobile applications has leaded to several shortages in Android system, such as the limitations in hardware resources, complex and frequent updating of the applications. In this paper, we propose a new kind of mobile application model named Streaming Application Model by absorbing the concept of Transparent Computing. The applications are modularized into some independent components and stored on server. The android devices load and launch the components dynamically. Streaming Application Model can reduce the consumption of hardware resources and take away the complex and frequent application update processes for users, thus improving the user experience in Android devices.
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- 2015
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18. Performance of polar codes on wireless communication channels
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Shengmei Zhao, Wenjuan Tang, Peng Shi, and Bei Wang
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Spatial correlation ,Theoretical computer science ,Computer science ,Error floor ,Concatenated error correction code ,Data_CODINGANDINFORMATIONTHEORY ,Topology ,Linear code ,Binary symmetric channel ,Turbo code ,Forward error correction ,Low-density parity-check code ,Error detection and correction ,Decoding methods ,Computer Science::Information Theory ,Rayleigh fading ,Communication channel - Abstract
Polar codes are a class of codes proposed currently, which can achieve the capacity of binary symmetric channel with low encoding and decoding algorithm. By generalizing the definition of Bhattacharyya parameter in discrete memoryless channel, we discuss the performance of polar codes on wireless communication channels in this paper. We present the special expressions of the parameters for continuous channels (Gaussian and Rayleigh fading channels), including the recursive formulas and the initial values, and we discuss the construction of polar codes for Gaussian and Rayleigh fading channels. We analyze the application of polar codes with the defined parameter over Rayleigh fading channel by transmitting image signals. The simulation results show that polar codes have better performance than that of low density parity-check codes (LDPC) codes with the same simulation condition. It is shown that polar codes will be a good candidate for wireless communication channels.
- Published
- 2012
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