作者机构：
[Ye, Xuming; Tian, Wenlong; Yang, Zhihuan] Univ South China, Sch Comp Sci & Technol, Hengyang, Peoples R China.;[Tian, Wenlong] Nanyang Technol Univ, Sch Phys & Math Sci, Singapore, Singapore.;[Li, Ruixuan] Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Wuhan, Peoples R China.;[Xu, Zhiyong] Suffolk Univ, Dept Math & Comp Sci, Boston, MA USA.
会议名称：
2024 IEEE 23rd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)
会议时间：
17 December 2024
会议地点：
Sanya, China
会议主办单位：
[Yang, Zhihuan;Tian, Wenlong;Ye, Xuming] Univ South China, Sch Comp Sci & Technol, Hengyang, Peoples R China.^[Tian, Wenlong] Nanyang Technol Univ, Sch Phys & Math Sci, Singapore, Singapore.^[Li, Ruixuan] Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Wuhan, Peoples R China.^[Xu, Zhiyong] Suffolk Univ, Dept Math & Comp Sci, Boston, MA USA.
会议论文集名称：
2024 IEEE 23rd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)
关键词：
Proof of Ownership;Cloud Storage;Discrete Logarithm Problem;Privacy-Preserving
摘要：
Cloud storage is widely used for flexible and efficient data management, yet redundant data across users requires storage optimization. Deduplication helps by storing only unique data, making data sharing and ownership verification essential post-deduplication. Current Proof of Ownership (PoW) methods rely on interactive communication, leading to delays and performance issues during intensive data operations, and often assume a level of trust that may not hold in practical scenarios. To overcome these issues, we propose ES-PoW, a non-interactive secure proof of ownership scheme for cloud storage. ES-PoW performs ownership verification in a single round, avoiding delays in block verification. Using modular exponentiation and the discrete logarithm problem, ES-PoW generates and verifies ownership proofs efficiently. Unlike previous schemes, ES-PoW is resilient against brute-force, replay, and Man-in-the-Middle attacks, without relying on trusted nodes, making it better suited to real-world applications. Experimental results show that ES-PoW reduces I/O operation time and accelerates computation, achieving up to 53.6× and 54.5× speed improvements over current methods.
作者机构：
[Tian, Wenlong; Yang, Zhihuan] Univ South China, Sch Comp Sci & Technol, Hengyang, Peoples R China.;[Tian, Wenlong] Nanyang Technol Univ, Sch Phys & Math Sci, Singapore, Singapore.;[Zhang, Emma] Needham High Sch, Needham, MA USA.;[Xu, Zhiyong] Suffolk Univ, Math & Comp Sci Dept, Boston, MA USA.
会议名称：
2024 IEEE 23rd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)
会议时间：
17 December 2024
会议地点：
Sanya, China
会议主办单位：
[Yang, Zhihuan;Tian, Wenlong] Univ South China, Sch Comp Sci & Technol, Hengyang, Peoples R China.^[Tian, Wenlong] Nanyang Technol Univ, Sch Phys & Math Sci, Singapore, Singapore.^[Zhang, Emma] Needham High Sch, Needham, MA USA.^[Xu, Zhiyong] Suffolk Univ, Math & Comp Sci Dept, Boston, MA USA.
会议论文集名称：
2024 IEEE 23rd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)
关键词：
Cloud Storage;Encrypted Data Reduction;Zero-Knowledge Proof
摘要：
With the widespread adoption of cloud storage, effectively identifying and eliminating redundant data among users while ensuring data security has become a significant challenge. However, traditional similarity detection methods has limitations in privacy protection. Although conventional encryption techniques can safeguard privacy, they have difficulty detecting redundancy between similar blocks. Thus, we propose a secure reduction of redundant and similar data for cloud storage to address these challenges based on zero-knowledge proof (Sec-Reduce), called Sec-Reduce. It first employs a novel zero-knowledge proof technique for file-level redundancy detection, where redundant files are identified and excluded from storage. To further determine the similarity of non-redundant files, the scheme performs content-based chunking and feature extraction using a similarity feature extraction method. These extracted features are then encrypted using the approximate homomorphic encryption scheme Cheon-Kim-Kim-Song (CKKS) to enable similarity detection in the ciphertext environment. Finally, secure delta encoding is applied to store unique ciphertext blocks and deltas. Evaluations of real-world datasets demonstrate that Sec-Reduce achieves higher storage savings than existing encrypted storage methods, with storage overhead comparable to plaintext storage and only moderate performance overhead.
期刊：
2023 IEEE 22ND INTERNATIONAL CONFERENCE ON TRUST, SECURITY AND PRIVACY IN COMPUTING AND COMMUNICATIONS, TRUSTCOM, BIGDATASE, CSE, EUC, ISCI 2023,2024年:833-840 ISSN：2324-898X
通讯作者：
Tian, WL
作者机构：
[Ye, Xuming; Tian, Wenlong; Wang, Jinzhao; Wan, Yaping] Univ South China, Sch Comp Sci & Technol, Hengyang, Peoples R China.;[Tian, Wenlong] Nanyang Technol Univ, Sch Phys & Math Sci, Singapore, Singapore.;[Li, Ruixuan] Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Wuhan, Peoples R China.;[Tang, Junwei] Wuhan Text Univ, Sch Comp Sci & Artificial Intelligence, Wuhan, Peoples R China.;[Xu, Zhiyong] Suffolk Univ, Math & Comp Sci Dept, Boston, MA 02114 USA.
通讯机构：
[Tian, WL ] U;Univ South China, Sch Comp Sci & Technol, Hengyang, Peoples R China.;Nanyang Technol Univ, Sch Phys & Math Sci, Singapore, Singapore.
会议名称：
2023 IEEE 22nd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)
会议时间：
01 November 2023
会议地点：
Exeter, United Kingdom
会议主办单位：
[Wang, Jinzhao;Tian, Wenlong;Ye, Xuming;Wan, Yaping] Univ South China, Sch Comp Sci & Technol, Hengyang, Peoples R China.^[Tian, Wenlong] Nanyang Technol Univ, Sch Phys & Math Sci, Singapore, Singapore.^[Li, Ruixuan] Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Wuhan, Peoples R China.^[Tang, Junwei] Wuhan Text Univ, Sch Comp Sci & Artificial Intelligence, Wuhan, Peoples R China.^[Xu, Zhiyong] Suffolk Univ, Math & Comp Sci Dept, Boston, MA 02114 USA.
会议论文集名称：
2023 IEEE 22nd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)
摘要：
With the increasing number of big data applications, large amounts of valuable data are distributed in different organizations or regions. Federated Learning (FL) enables collaborative model training without sharing sensitive data and is widely used in AI medical diagnosis, economy, and autonomous driving scenarios. However, it still leaks the privacy from the gradient exchange in federated learning. What's worse, state-of-the-art work, such as Batchcrypt, still suffers from computational overhead due to a considerable amount of computation and communication costs caused by homomorphic encryption. Therefore, we propose a novel symmetric key-based homomorphic encryption scheme, Sym-Fed. To unleash the power of symmetric encryption in federated learning, we combine random masking with symmetric encryption and keep the homomorphic property during the gradient exchange in the federated learning process. Finally, the security analysis and experimental results on real workloads show that our design achieves performance improvement 6x to 668x and reduces the communication overhead 1.2x to 107x compared with the state-of-the-art work, BatchCrypt and FATE, without model accuracy degradation and security compromise.
期刊：
2022 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT),2022年:21-29
通讯作者：
Tian, WL
作者机构：
[Ouyang, Chunping; Tian, Wenlong; Liu, Yongbin; Liu, Qifei; Li, Jing; Geng, Yuqing] Univ South China, Sch Comp Sci & Technol, Hengyang, Peoples R China.;[Tian, Wenlong] Nanyang Technol Univ, Sch Phys & Math Sci, Singapore, Singapore.;[Ouyang, Chunping; Tian, Wenlong; Liu, Yongbin] Hunan Prov Base Sci & Technol Innovat Cooperat, Hengyang, Peoples R China.;[Li, Ruixuan] Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Wuhan, Peoples R China.;[Xiao, Weijun] Virginia Commonwealth Univ, Elect & Comp Engn, Richmond, VA 23284 USA.
通讯机构：
[Tian, WL ] U;Univ South China, Sch Comp Sci & Technol, Hengyang, Peoples R China.;Nanyang Technol Univ, Sch Phys & Math Sci, Singapore, Singapore.;Hunan Prov Base Sci & Technol Innovat Cooperat, Hengyang, Peoples R China.
会议名称：
2022 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT)
会议时间：
December 2022
会议地点：
Vancouver, WA, USA
会议主办单位：
[Geng, Yuqing;Tian, Wenlong;Ouyang, Chunping;Liu, Yongbin;Liu, Qifei;Li, Jing] Univ South China, Sch Comp Sci & Technol, Hengyang, Peoples R China.^[Tian, Wenlong] Nanyang Technol Univ, Sch Phys & Math Sci, Singapore, Singapore.^[Tian, Wenlong;Ouyang, Chunping;Liu, Yongbin] Hunan Prov Base Sci & Technol Innovat Cooperat, Hengyang, Peoples R China.^[Li, Ruixuan] Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Wuhan, Peoples R China.^[Xiao, Weijun] Virginia Commonwealth Univ, Elect & Comp Engn, Richmond, VA 23284 USA.^[Xu, Zhiyong] Suffolk Univ, Math & Comp Sci Dept, Boston, MA 02114 USA.
会议论文集名称：
2022 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT)
关键词：
Cloud Storage;Resemblance Detection;Context-Aware;Deduplication Ratio Prediction
摘要：
With the prevalence of cloud storage, people prefer to outsource their data to the cloud for flexibility and reliability. Undoubtedly, there are lots of redundancy among these data. However, high-end storage with deduplication costs heavy computation and increases the data management complexity. Potential customers need the redundancy proportion information of their outsourced data to decide whether high-end storage with deduplication is worthwhile. Thus, many researchers have previously attempted to predict the redundant ratio. However, existing mechanisms ignore the redundancy proportion among similar chunks containing many duplicate data. Although resemblance detection, detecting the duplicate parts among similar data, has become a hot issue, it is hardly applied to the conventional deduplication ratio estimation because of unacceptable calculation cost. Therefore, we analyze the limitations and challenges of deduplication ratio prediction in prediction scope and response time and further propose a novel prediction scheme. By leveraging the context-aware resemblance detection, and confidence interval theory, our method can achieve faster estimation speed with higher accuracy in deduplication ratio compared with the state-of-the-art work. Finally, the results show that our method can efficiently and effectively estimate the proportion of duplicate chunks and redundant data among similar chunks by conducting experiments on real workloads.
作者：
Ye, Xuming;Tang, Jia;Tian, Wenlong;Li, Ruixuan;Xiao, Weijun;...
作者机构：
[Ye, Xuming; Tian, Wenlong; Tang, Jia; Geng, Yuqing] Univ South China, Sch Comp Sci & Technol, Hengyang, Peoples R China.;[Li, Ruixuan] Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Wuhan, Peoples R China.;[Xiao, Weijun] Virginia Commonwealth Univ, Elect & Comp Engn, Richmond, VA 23284 USA.;[Xu, Zhiyong] Suffolk Univ, Math & Comp Sci Dept, Boston, MA USA.;[Xu, Zhiyong] Chinese Acad Sci, Shenzhen Inst Adv Technol, Beijing, Peoples R China.
会议名称：
2021 IEEE International Conference on Networking, Architecture and Storage (NAS)
会议时间：
October 2021
会议地点：
Riverside, CA, USA
会议主办单位：
[Ye, Xuming;Tang, Jia;Tian, Wenlong;Geng, Yuqing] Univ South China, Sch Comp Sci & Technol, Hengyang, Peoples R China.^[Li, Ruixuan] Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Wuhan, Peoples R China.^[Xiao, Weijun] Virginia Commonwealth Univ, Elect & Comp Engn, Richmond, VA 23284 USA.^[Xu, Zhiyong] Suffolk Univ, Math & Comp Sci Dept, Boston, MA USA.^[Xu, Zhiyong] Chinese Acad Sci, Shenzhen Inst Adv Technol, Beijing, Peoples R China.
会议论文集名称：
2021 IEEE International Conference on Networking, Architecture and Storage (NAS)
关键词：
Resemblance Data Deduplication;Cloud Storage;Metadata Size;Variabe-Grained
摘要：
With the prevalence of cloud storage, data deduplication has been a widely used technology by removing cross users' duplicate data and saving network bandwidth. Nevertheless, traditional data deduplication hardly detects duplicate data among resemblance chunks. Currently, a resemblance data deduplication, called Finesse, has been proposed to detect and remove the duplicate data among similar chunks efficiently. However, we observe that the chunks following the similar chunk have a high chance of resembling data locality property, and vice versa. Processing these adjacent similar chunks in small average chunk size level increases the metadata, which deteriorates the deduplication system performance. Moreover, existing resemblance data deduplication schemes ignore the performance impact from metadata. Therefore, we propose a fast variable-grained resemblance data deduplication for cloud storage. It dynamically combines the adjacent resemblance chunks or unique chunks or breaks those chunks, located at the transition region between resemblance chunks and unique chunks. Finally, we implement a prototype and conduct a serial of experiments on real-world datasets. The results show that our method dramatically reduces the metadata size while achieving the high deduplication ratio.
摘要：
The batch verification methods can relieve the bottleneck problem of vehicular authentication efficiency to accelerate the authentication speed during the group construction phase. However, in current batch verification methods, there exist some deficiencies such as the authentication methods applied in the actual scenario are sensitive to the group initialization and dynamic construction process and the intra-group communication efficiency have not been improved due to lack of effective dynamic group management. This paper proposes a fast and secure batch verification scheme based on dynamic group management and certificateless public-key cryptography. First, the two-way authentication between the proxy vehicle and the roadside unit (RSU) is established, and then the batch verification method relying on RSU-assisted are proposed based on dynamic management of the intra-group vehicles. Our method fully considers the security requirements of the group while solving certification efficiency. Theoretical analysis and simulation experiments show that compared with other existing solutions, the verification scheme in this paper reduces the computational delay and transmission overhead whilst increasing robustness in dynamic environments.
会议论文集名称：
DEStech Transactions on Computer Science and Engineering
关键词：
Complex networks;Routing strategy;Effective path;Local visibility and Detecting communities
摘要：
A routing strategy using nodes' effective path and dynamic information weight distribution was proposed, which based on the improved efficient path routing strategy in complex network. The strategy's weight factor was optimized by the new strategy, Considering the insufficient of local routing strategies in global performance and the community division technology in complex network, a kind of local visibility routing strategy according to the thought of community division was presented. On the condition of community division of the Bgll condensation algorithm, the topology structure information of neighbor nodes and all the community nodes were only stored in each node's routing list in the new strategy. When node transaction in community, global routing strategy was adopted and when between communities, minimum load strategy will be used. The simulation results indicate that in the different connection density networks, the performance of the new strategy achieves the best when community division's degree of modularization is the highest. Contrasted to other local routing strategy, the optimized strategy holds incomparable superiority on throughput, transmission delay and the packet loss.
通讯机构：
[Ouyang, Chunping] U;Univ South China, Sch Comp Sci & Technol, Hengyang 421001, Peoples R China.
会议名称：
2018 14th International Conference on Semantics, Knowledge and Grids (SKG)
会议时间：
September 2018
会议地点：
Guangzhou, China
会议主办单位：
[Chen, Xianglong;Ouyang, Chunping;Liu, Yongbin;Luo, Lingyun;Yang, Xiaohua] Univ South China, Sch Comp Sci & Technol, Hengyang 421001, Peoples R China.
会议论文集名称：
2018 14th International Conference on Semantics, Knowledge and Grids (SKG)
关键词：
Text classification;Deep learning;CNN;RNN
摘要：
Deep learning has shown its effectiveness in many tasks such as text classification and computer vision. Most text classification tasks are concentrated in the use of convolution neural network and recurrent neural network to obtain text feature representation. In some researches, Attention mechanism is usually adopted to improve classification accuracy. According to the target of task 6 in NLP&CC2018, a hybrid deep learning model which combined BiGRU, CNN and Attention mechanism was proposed to improve text classification. The experimental results show that the Fl-score of the proposed model successfully excels the task's baseline model. Besides, this hybrid Deep Learning model gets higher Precision, Recall and Fl-score comparing with some other popular Deep Learning models, and the improvement of on Fl-score is 5.4% than the single CNN model.
会议论文集名称：
2018 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)
摘要：
This paper presents an efficiently wireless ECG acquisition system with lossless data compressing algorithm. The proposed algorithm were evaluated by using all patterns from MIT-BIH arrhythmia database, and the analytic result shows that the proposed lossless algorithm achieves the CR value of 2.64 in average. Therefore, the proposed algorithm has very outstanding performance.
会议论文集名称：
2018 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)
摘要：
The unmanned aerial vehicle (UAV) has been continuously developed for military uses, agricultural applications and various livelihood activities. This study proposes a moving object tracking algorithm suitable for UAV that help it automatically tracks the designated object. The prototype algorithm can be divided into three steps: 1) object detection; 2) object feature pixel extraction; and 3) feature pixel matching. The experimental result shows that the execution time can be achieved up to 24 fps under the resolution of 1280x720.
期刊：
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),2017年10561 LNCS:1-13 ISSN：0302-9743
摘要：
It has long been known that there are software applications for which it is difficult to detect subtle errors, faults, defects, or anomalies because there is no reliable "test oracle" to indicate what the correct output should be for arbitrary input. The absence of a test oracle clearly presents a challenge in testing the software applications of scientific computing from the domain of nuclear power plant. Metamorphic testing has been shown to be a simple yet effective technique in addressing the quality assurance of these "non-testable programs." In this paper, we introduce Metamorphic testing method to address the oracle problem as mentioned above. We identify a metamorphic relation for a real-world scientific computing programs which do not have test oracles, and demonstrate the effectiveness of metamorphic testing in identifying the error.
期刊：
PROCEEDINGS OF THE 25TH INTERNATIONAL CONFERENCE ON NUCLEAR ENGINEERING, 2017, VOL 2,2017年2
通讯作者：
Li Meng
作者机构：
[Li Meng; Yang Xiao-Hua] CNNC Key Lab High Trusted Comp, Hengyang 421001, Hunan, Peoples R China.;[Li Yu-Yan; Wang Li-Jun] Univ South China, Sch Elect Engn, Hengyang 421001, Hunan, Peoples R China.
通讯机构：
[Li Meng] C;CNNC Key Lab High Trusted Comp, Hengyang 421001, Hunan, Peoples R China.
会议名称：
2017 25th International Conference on Nuclear Engineering
摘要：
In view of the characteristics of the physical code Nestor the focus is on the correctness of calculation for which the test adequacy criterion has been established. This is based on structural coverage and the input domain. According to such test adequacy criterion, testing strategies have been applied on the entire testing process. They consist of unit static, unit dynamic, integration, system and regression test strategy. Each strategy is composed of test target, test range, technology and method, entry criterion, completion criterion, test focus and priority. After compared with 11 basic benchmarks from nuclear power plants and calculation result of benchmark programs, the ELEMENT program result is correct and credible; the relative error of result is less than three percent. The ELEMENT testing is adequacy. Its test cases covers fuel grid element types, fuel types, non-combustible grid element types, and control rod computational models. Furthermore, it puts forward a research direction in the future.
摘要：
Image annotation is a task of assigning semantic labels to an image. Recently, deep neural networks with visual attention have been utilized successfully in many computer vision tasks. In this paper, we show that conventional attention mechanism is easily misled by the salient class, i.e., the attended region always contains part of the image area describing the content of salient class at different attention iterations. To this end, we propose a novel attention shaping mechanism, which aims to maximize the non-overlapping area between consecutive attention processes by taking into account the history of previous attention vectors. Several weighting polices are studied to utilize the history information in different manners. In two benchmark datasets, i.e., PASCAL VOC2012 and MIRFlickr-25k, the average precision is improved by up to 10% in comparison with the state-of-the-art annotation methods.
期刊：
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),2016年10102:549-558 ISSN：0302-9743
通讯机构：
[Ouyang, Chunping] U;Univ South China, Sch Comp Sci & Technol, Hengyang 421001, Peoples R China.
会议名称：
5th International Conference on Natural Language Processing and Chinese Computing (NLPCC) / 24th International Conference on Computer Processing of Oriental Languages (ICCPOL)
会议时间：
DEC 02-06, 2016
会议地点：
Kunming Univ Sci & Technol, Kunming, PEOPLES R CHINA
会议主办单位：
Kunming Univ Sci & Technol
会议论文集名称：
Lecture Notes in Computer Science
关键词：
Artificial intelligence;Computer science;Computers;Correlation methods;Classic algorithm;Concept description;HowNet;nocv1;Pearson correlation coefficients;Popular platform;Similarity algorithm;Similarity evaluation;Words similarity;Correlation methods;Natural language processing systems
