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Wu, Hanqing, Cao, Jiannong, Yang, Yanni, Tung, Cheung Leong, Jiang, Shan, Tang, Bin, Liu, Yang, Wang, Xiaoqing, Deng, Yuming.  2019.  Data Management in Supply Chain Using Blockchain: Challenges and a Case Study. 2019 28th International Conference on Computer Communication and Networks (ICCCN). :1–8.

Supply chain management (SCM) is fundamental for gaining financial, environmental and social benefits in the supply chain industry. However, traditional SCM mechanisms usually suffer from a wide scope of issues such as lack of information sharing, long delays for data retrieval, and unreliability in product tracing. Recent advances in blockchain technology show great potential to tackle these issues due to its salient features including immutability, transparency, and decentralization. Although there are some proof-of-concept studies and surveys on blockchain-based SCM from the perspective of logistics, the underlying technical challenges are not clearly identified. In this paper, we provide a comprehensive analysis of potential opportunities, new requirements, and principles of designing blockchain-based SCM systems. We summarize and discuss four crucial technical challenges in terms of scalability, throughput, access control, data retrieval and review the promising solutions. Finally, a case study of designing blockchain-based food traceability system is reported to provide more insights on how to tackle these technical challenges in practice.

Zhu, Weijun, Liu, Yichen, Fan, Yongwen, Liu, Yang, Liu, Ruitong.  2019.  If Air-Gap Attacks Encounter the Mimic Defense. 2019 9th International Conference on Information Science and Technology (ICIST). :485—490.
Air-gap attacks and mimic defense are two emerging techniques in the field of network attack and defense, respectively. However, direct confrontation between them has not yet appeared in the real world. Who will be the winner, if air-gap attacks encounter mimic defense? To this end, a preliminary analysis is conducted for exploring the possible the strategy space of game according to the core principles of air-gap attacks and mimic defense. On this basis, an architecture model is proposed, which combines some detectors for air-gap attacks and mimic defense devices. First, a Dynamic Heterogeneous Redundancy (DHR) structure is employed to be on guard against malicious software of air-gap attacks. Second, some detectors for air-gap attacks are used to detect some signal sent by air-gap attackers' transmitter. Third, the proposed architecture model is obtained by organizing the DHR structure and the detectors for air-gap attacks with some logical relationship. The simulated experimental results preliminarily confirm the power of the new model.
Ma, Zhuo, Liu, Yang, Liu, Ximeng, Ma, Jianfeng, Li, Feifei.  2019.  Privacy-Preserving Outsourced Speech Recognition for Smart IoT Devices. IEEE Internet of Things Journal. 6:8406–8420.
Most of the current intelligent Internet of Things (IoT) products take neural network-based speech recognition as the standard human-machine interaction interface. However, the traditional speech recognition frameworks for smart IoT devices always collect and transmit voice information in the form of plaintext, which may cause the disclosure of user privacy. Due to the wide utilization of speech features as biometric authentication, the privacy leakage can cause immeasurable losses to personal property and privacy. Therefore, in this paper, we propose an outsourced privacy-preserving speech recognition framework (OPSR) for smart IoT devices in the long short-term memory (LSTM) neural network and edge computing. In the framework, a series of additive secret sharing-based interactive protocols between two edge servers are designed to achieve lightweight outsourced computation. And based on the protocols, we implement the neural network training process of LSTM for intelligent IoT device voice control. Finally, combined with the universal composability theory and experiment results, we theoretically prove the correctness and security of our framework.
Su, Jinsong, Zeng, Jiali, Xiong, Deyi, Liu, Yang, Wang, Mingxuan, Xie, Jun.  2018.  A Hierarchy-to-Sequence Attentional Neural Machine Translation Model. IEEE/ACM Transactions on Audio, Speech, and Language Processing. 26:623—632.

Although sequence-to-sequence attentional neural machine translation (NMT) has achieved great progress recently, it is confronted with two challenges: learning optimal model parameters for long parallel sentences and well exploiting different scopes of contexts. In this paper, partially inspired by the idea of segmenting a long sentence into short clauses, each of which can be easily translated by NMT, we propose a hierarchy-to-sequence attentional NMT model to handle these two challenges. Our encoder takes the segmented clause sequence as input and explores a hierarchical neural network structure to model words, clauses, and sentences at different levels, particularly with two layers of recurrent neural networks modeling semantic compositionality at the word and clause level. Correspondingly, the decoder sequentially translates segmented clauses and simultaneously applies two types of attention models to capture contexts of interclause and intraclause for translation prediction. In this way, we can not only improve parameter learning, but also well explore different scopes of contexts for translation. Experimental results on Chinese-English and English-German translation demonstrate the superiorities of the proposed model over the conventional NMT model.

Chen, Sen, Fan, Lingling, Meng, Guozhu, Su, Ting, Xue, Minhui, Xue, Yinxing, Liu, Yang, Xu, Lihua.  2020.  An Empirical Assessment of Security Risks of Global Android Banking Apps. 2020 IEEE/ACM 42nd International Conference on Software Engineering (ICSE). :1310—1322.
Mobile banking apps, belonging to the most security-critical app category, render massive and dynamic transactions susceptible to security risks. Given huge potential financial loss caused by vulnerabilities, existing research lacks a comprehensive empirical study on the security risks of global banking apps to provide useful insights and improve the security of banking apps. Since data-related weaknesses in banking apps are critical and may directly cause serious financial loss, this paper first revisits the state-of-the-art available tools and finds that they have limited capability in identifying data-related security weaknesses of banking apps. To complement the capability of existing tools in data-related weakness detection, we propose a three-phase automated security risk assessment system, named Ausera, which leverages static program analysis techniques and sensitive keyword identification. By leveraging Ausera, we collect 2,157 weaknesses in 693 real-world banking apps across 83 countries, which we use as a basis to conduct a comprehensive empirical study from different aspects, such as global distribution and weakness evolution during version updates. We find that apps owned by subsidiary banks are always less secure than or equivalent to those owned by parent banks. In addition, we also track the patching of weaknesses and receive much positive feedback from banking entities so as to improve the security of banking apps in practice. We further find that weaknesses derived from outdated versions of banking apps or third-party libraries are highly prone to being exploited by attackers. To date, we highlight that 21 banks have confirmed the weaknesses we reported (including 126 weaknesses in total). We also exchange insights with 7 banks, such as HSBC in UK and OCBC in Singapore, via in-person or online meetings to help them improve their apps. We hope that the insights developed in this paper will inform the communities about the gaps among multiple stakeholders, including banks, academic researchers, and third-party security companies.