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arXiv 2020年

作者： Weng, Jiasi Weng, Jian Cai, Chengjun Huang, Hongwei Wang, Cong The College of Information Science and Technology College of Cyber security National Joint Engineering Research Center of Network Security Detection and Protection Technology Guangdong Key Laboratory of Data Security and Privacy Preserving Jinan University Guangzhou510632 China The Department of Computer Science City University of Hong Kong Hong Kong Hong Kong The City University of Hong Kong Shenzhen Research Institute Shenzhen518057 China

ML-as-a-service (MLaaS) becomes increasingly popular and revolutionizes the lives of people. A natural requirement for MLaaS is, however, to provide highly accurate prediction services. To achieve this, current MLaaS systems integrate and combine multiple well-trained models in their services. Yet, in reality, there is no easy way for MLaaS providers, especially for startups, to collect sufficiently well-trained models from individual developers, due to the lack of incentives. In this paper, we aim to fill this gap by building up a model marketplace, called as Golden Grain, to facilitate model sharing, which enforces the fair model-money swapping process between individual developers and MLaaS providers. Specifically, we deploy the swapping process on the blockchain, and further introduce a blockchain-empowered model benchmarking process for transparently determining the model prices according to their authentic performances, so as to motivate the faithful contributions of well-trained models. Especially, to ease the blockchain overhead for model benchmarking, our marketplace carefully offloads the heavy computation and designs a secure off-chain on-chain interaction protocol based on a trusted execution environment (TEE), for ensuring both the integrity and authenticity of benchmarking. We implement a prototype of our Golden Grain on the Ethereum blockchain, and conduct extensive experiments using standard benchmark datasets to demonstrate the practically affordable performance of our design. © 2020, CC BY.

关键词： Blockchain

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A new dance generation task from start to end

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Journal of Physics: Conference Series 2020年第1期1651卷

作者： Xiao Xu Lijuan Duan Faculty of Information Technology Beijing University of Technology Beijing 100124 China Beijing Key Laboratory of Trusted Computing Beijing 100124 China National Engineering Laboratory for Critical Technologies of Information Security Classified Protection Beijing 100124 China

The dance generation task is an interesting task. The general task is to predict the next dance sequence through a given initial frame, or to convert from music to dance through audio information. However, these works cannot control the direction of dance generation and lack suitable ground truth to evaluate the generated results. In our work, we give the start frame and the end frame, which can effectively solve the above problems, and can also be understood as a standardized new task. Our experimental results show that through a two-stream CNN network, we can well complete the new task that we proposed.

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Secure personal data sharing for simultaneous, parallel or sequential processing service: Autonomously and controllably

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Future Generation Computer Systems 2025年 173卷

作者： Lyu, Qiuyun Zhou, Yilong Ren, Yizhi Zhou, Lingfei Wu, Zekai Zhao, Chengyao Zhang, Jilin Ma, Duohe School of Cyberspace Hangzhou Dianzi University Zhejiang Hangzhou310018 China Zhejiang Provincial Key Laboratory of Data Storage Transmission and Application Technology Zhejiang Hangzhou310018 China Pinghu Digital Technology Innovation Institute Co. Ltd Hangzhou Dianzi University Zhejiang Jiaxing314299 China Zhejiang Provincial Key Laboratory of Sensitive Data Security Protection and Confidentiality Governance Zhejiang Hangzhou310018 China Zhejiang Big Data Development Center Zhejiang Hangzhou310018 China Zhejiang Data Bureau Zhejiang Hangzhou310018 China Institute of Information Engineering of Chinese Academy of Sciences Beijing100084 China

Personal data, as an important category of data elements of a trusted data circulation, needs to be shared to others in "one-to-one" mode or "one-to-many" mode with simultaneously, parallelly or sequentially to meet all kinds of complex business scenarios. Actually, today's application asks more personalized content and more often than ever before, it increasingly highlights the need for data owners to have privacy rights such as the right to be informed and the right to erasure. However, existing personal data sharing schemes mainly deal with "one-to-one" mode and some of them have made breakthroughs in "one-to-many" mode, they still do not support complex scenarios such as simultaneously operating, parallelly executing or sequentially processing in a data privacy protection way. Therefore, this paper first encapsulates personal data into data capsules and designs three types of access tasks based on the requirements of complex scenarios to achieve secure collaborative sharing, secure multi-copies sharing, and secure ordered sharing. Specifically, the scheme embeds fine-grained authorization mechanisms within the data capsules and combines them with access task tokens that support autonomous control, achieving personal data sharing autonomy, including selective sharing, informed consent authorization, and permission revocation. Finally, the security of the proposed scheme is proven through formal and informal security proofs. The results of performance analysis show that the proposed scheme has better computational and storage efficiency while supporting the data sharing needs of complex scenarios. © 2025 Elsevier B.V.

关键词： Data Sharing

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EG-GAN: Cross-language emotion gain synthesis based on cycle-consistent adversarial networks

arXiv

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arXiv 2019年

作者： Jia, Xiaoqi Tai, Jianwei Huang, Qingjia Li, Yakai Zhang, Weijuan Du, Haichao Key Laboratory of Network Assessment Technology Institute of Information Engineering CAS Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Beijing Key Laboratory of Network Security and Protection Technology

Despite remarkable contributions from existing emotional speech synthesizers, we find that these methods are based on Text-to-Speech system or limited by aligned speech pairs, which suffered from pure emotion gain synthesis. Meanwhile, few studies have discussed the cross-language generalization ability of above methods to cope with the task of emotional speech synthesis in various languages. We propose a cross-language emotion gain synthesis method named EG-GAN which can learn a language-independent mapping from source emotion domain to target emotion domain in the absence of paired speech samples. EG-GAN is based on cycle-consistent generation adversarial network with a gradient penalty and an auxiliary speaker discriminator. The domain adaptation is introduced to implement the rapid migrating and sharing of emotional gains among different languages. The experiment results show that our method can efficiently synthesize high quality emotional speech from any source speech for given emotion categories, without the limitation of language differences and aligned speech pairs. Copyright © 2019, The Authors. All rights reserved.

关键词： Linguistics

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B2SFinder: Detecting Open-Source Software Reuse in COTS Software

B2SFinder: Detecting Open-Source Software Reuse in COTS Soft...

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IEEE International Conference on Automated Software engineering (ASE)

作者： Zimu Yuan Muyue Feng Feng Li Gu Ban Yang Xiao Shiyang Wang Qian Tang He Su Chendong Yu Jiahuan Xu Aihua Piao Jingling Xuey Wei Huo Institute of Information Engineering Chinese Academy of Sciences Key Laboratory of Network Assessment Technology Chinese Academy of Sciences Beijing Key Laboratory of Network Security and Protection Technology and School of Cyber Security University of Chinese Academy of Sciences University of New South Wales

COTS software products are developed extensively on top of OSS projects, resulting in OSS reuse vulnerabilities. To detect such vulnerabilities, finding OSS reuses in COTS software has become imperative. While scalable to tens of thousands of OSS projects, existing binary-to-source matching approaches are severely imprecise in analyzing COTS software products, since they support only a limited number of code features, compute matching scores only approximately in measuring OSS reuses, and neglect the code structures in OSS projects. We introduce a novel binary-to-source matching approach, called B2SFINDER 1 , to address these limitations. First of all, B2SFINDER can reason about seven kinds of code features that are traceable in both binary and source code. In order to compute matching scores precisely, B2SFINDER employs a weighted feature matching algorithm that combines three matching methods (for dealing with different code features) with two importance-weighting methods (for computing the weight of an instance of a code feature in a given COTS software application based on its specificity and occurrence frequency). Finally, B2SFINDER identifies different types of code reuses based on matching scores and code structures of OSS projects. We have implemented B2SFINDER using an optimized data structure. We have evaluated B2SFINDER using 21991 binaries from 1000 popular COTS software products and 2189 candidate OSS projects. Our experimental results show that B2SFINDER is not only precise but also scalable. Compared with the state ofthe art, B2SFINDER has successfully found up to 2.15× as many reuse cases in 53.85 seconds per binary file on average. We also discuss how B2SFINDER can be leveraged in detecting OSS reuse vulnerabilities in practice.

关键词： Open source software Libraries Software algorithms security Switches Arrays

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Privacy-preserving medical treatment system through nondeterministic finite automata

arXiv

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arXiv 2020年

作者： Yang, Yang Deng, Robert H. Liu, Ximeng Wu, Yongdong Weng, Jian Zheng, Xianghan Rong, Chunming School of Information Systems Singapore Management University Singapore College of Mathematics and Computer Science Fuzhou University Fujian China State Key Laboratory of Integrated Services Networks Xidian University Guangdong Provincial Key Laboratory of Data Security and Privacy Protection Guangzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China School of Information Systems Singapore Management University Singapore Singapore Department of Computer Science Jinan University Guangdong China College of Mathematics and Computer Science Fuzhou University Fujian China Department of Electronic Engineering and Computer Science University of Stavanger Norway MingByte Technology Qingdao China

In this paper, we propose a privacy-preserving medical treatment system using nondeterministic finite automata (NFA), hereafter referred to as P-Med, designed for the remote medical environment. P-Med makes use of the nondeterministic transition characteristic of NFA to flexibly represent the medical model, which includes illness states, treatment methods and state transitions caused by exerting different treatment methods. A medical model is encrypted and outsourced to the cloud to deliver telemedicine services. Using P-Med, patient-centric diagnosis and treatment can be made on-the-fly while protecting the confidentiality of a patient's illness states and treatment recommendation results. Moreover, a new privacy-preserving NFA evaluation method is given in P-Med to get a confidential match result for the evaluation of an encrypted NFA and an encrypted data set, which avoids the cumbersome inner state transition determination. We demonstrate that P-Med realizes treatment procedure recommendation without privacy leakage to unauthorized parties. We conduct extensive experiments and analyses to evaluate efficiency. Copyright © 2020, The Authors. All rights reserved.

关键词： Diseases

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A Novel Video Encryption Method Based on Faster R-CNN

A Novel Video Encryption Method Based on Faster R-CNN

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2018 International Conference on Computer Science, Electronics and Communication engineering（CSECE2018）

作者： Lijuan Duan Dongkui Zhang Fan Xu Guoqin Cui Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Trusted Computing National Engineering Laboratory for Critical Technologies of Information Security Classified Protection State Key Laboratory of Digital Multi-media Chip Technology Vimicro Corporation

ISBN: (纸本)9781510862593

In order to improve the generalization performance of video encryption and reduce the amount of data in video en-cryption, this paper proposes a video encryption on regions of interest(ROI) method based on Faster R-CNN by combining machine learning with information security. The method trains a Faster R-CNN model using the ROI dataset firstly, and then uses the model to extract ROI in the video. Different encryption algorithms are used to encrypt ROI and non-ROI in the video respectively. To overcome the shortcomings of encryption algorithms that can only be used for a specific coded video, a special video encryption method is proposed to encrypt the video with different video coding structure and has better generalization performance. Compared with the encryption method in the video coding process, this method considers the content information of the video fully and has better performance. It can be concluded through experiments that the encryption method in this paper has the characteristics of higher security and less calculation.

关键词： video encryption faster R-CNN the ROI of video

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Evolutionary game based analysis for user privacy protection behaviors in social networks 3

Evolutionary game based analysis for user privacy protection...

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3rd IEEE International Conference on Data Science in Cyberspace, DSC 2018

作者： Liu, Fang Pan, Li Yao, Li-Hong School of Cyber Security Shanghai Jiao Tong University Shanghai200240 China Shanghai Key Laboratory of Integrated Administration Technologies for Information Security Shanghai200240 China National Engineering Laboratory for Information Content Analysis Technology Shanghai China

ISBN: (纸本)9781538642108

With the increasing development of online social networks, privacy protection attracts more and more concerns. Most research on privacy protection focus on specific techniques, and they assume users adopt security protection mechanisms for granted. The impact of user behaviors on the deployment of these mechanisms is usually neglected. In fact, the actual effect of privacy protection mechanisms depends heavily on user behaviors. Whether a user adopts the security mechanism is affected by incentives, interactions with friends and other factors. It is necessary to encourage users to take privacy protection settings in order to improve the security environment in social networks. Therefore, in this paper, a network evolutionary game model is proposed for studying the influence of user behaviors on the deployment of privacy protection mechanisms, where whether to take privacy settings is used as game strategies. Considering real interactions in social networks, games only happen between connected users in the model. Then the dynamic equation of user behaviors over time is derived. Based on the dynamic equation, evolutionary stability conditions are proven according to the benefit cost ratio of taking the protection. Experiments in real social networks verify the feasibility of the proposed model. The analysis results are helpful for guiding security services to promote the deployment of privacy protection mechanisms in social networks. © 2018 IEEE.

关键词： Social networking (online)

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Privacy-aware personal information propagation management in social networks 3

Privacy-aware personal information propagation management in...

引用

3rd IEEE International Conference on Data Science in Cyberspace, DSC 2018

作者： Wu, Yu Pan, Li School of Cyber Security Shanghai Jiao Tong University Shanghai200240 China Shanghai Key Laboratory of Integrated Administration Technologies for Information Security Shanghai200240 China National Engineering Laboratory for Information Content Analysis Technology Shanghai China

ISBN: (纸本)9781538642108

Online social network becomes the most important medium of information propagation. As the number of social network users grows rapidly and information sharing behavior becomes more frequent, the propagation range of personal information becomes more widespread. This phenomenon gradually arouses the user's high concern for the privacy protection of personal information. At present, the main contradiction is formed between users' privacy awareness demands for information sharing and unknowns of information propagation range. In order to solve this contradiction, an open framework called PPIPM(Privacy-aware Personal information Propagation Management) is proposed in this paper. It measures user's privacy awareness from the perspective of dynamic propagation, and then achieves privacy protection for sharing personal information by propagation constraints based on RBAC(Role Based Access Control) model. Role mining and permission assignment are two key technologies in deployment of PPIPM, and they are implemented in a separate manner with relative independence, respectively called RMPM (Role Mining for Propagation Management) and MAPM (Maximum-permission Authorization for Propagation Management). The Facebook dataset is used in experiments to evaluate the feasibility of PPIPM and compare the effect of personal information propagation management while plugging different role mining algorithms into PPIPM. The results indicate that PPIPM has good applicability for satisfying users' privacy awareness demands in general social networks. © 2018 IEEE.

关键词： information dissemination

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Blockchain-Based Incentive Announcement System for Internet of Vehicles

Blockchain-Based Incentive Announcement System for Internet ...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Yang Yang Jialiang Chen Xianghan Zheng Ximeng Liu Wenzhong Guo Hairong Lv College of Mathematics and Computer Science Fuzhou University Fuzhou China Guangxi Key Laboratory of Cryptography and Information Security Guangnxi China Guangdong Provincial Key Laboratory of Data Security and Privacy Protection Guangzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University) Fuzhou China Ministry of Education Key Laboratory of Bioinformatics Bioinformatics Division and Center for Synthetic and Systems Biology Beijing National Research Center for Information Science and TechnologyDepartment of Automation Tsinghua University Beijing China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

With the rapid development of computation and communication technologies, the traditional vehicle ad hoc networks (VANETs) are changing to Internet of vehicle (IoV). Vehicular announcement networks in IoV have been widely used in the communication of vehicles. Generally, we need to solve two problems while establishing a vehicular announcement system. First, we need to protect user's privacy when broadcasting the message. Second, participants usually lack the enthusiasm to reply to the announcement. To solve these two problems, we propose a novel blockchain-based incentive announcement system that not only allows participants to anonymously announce their message on the blockchain in a non-trusted environment, but also motivates witnesses to respond to the request of the traffic information with incentive mechanism. Meanwhile, traffic messages and signatures in our system are tamper-resistant, which are recorded on the blockchain. According to the security and performance analysis, it shows that our system is privacy-preserving and efficient in computation cost.

关键词： Blockchain Privacy Smart contracts Authentication Internet Remuneration Hash functions

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