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PKEST: Public-Key Encryption With Similarity Test for Medical Consortia Cloud Computing

IEEE Transactions on Cloud Computing 2025年第2期13卷 680-693页

作者： Chen, Junsong Zeng, Shengke Han, Song Yin, Jin Chen, Peng Xihua University School of Computer and Software Engineering Chengdu610039 China Hangzhou City University School of Computer and Computing Science Hangzhou310015 China Sichuan University West China Biomedical Big Data Center West China Hospital Chengdu610041 China

Cloud computing eliminates the limitations of local hardware architecture while also enabling rapid data sharing between healthcare institutions. Encryption of electronic medical records (EMRs) before uploading to cloud servers is necessary for privacy. However, encryption brings challenges for computation. Public Key Encryption with Equality Test (PKEET) allows cloud servers to test the underlying message equality without decryption. Therefore, it can be used to classify the encrypted EMRs corresponding to different medical symptoms. However, traditional PKEETs have limitations in testing the similarity between the ciphertexts. Undoubtedly, it can not handle EMR classification with similar medical symptoms efficiently. In this work, we propose a lightweight public key encryption with similarity test (PKEST) for the EMR classification shared in medical consortia. Our scheme can resist offline message recovery attacks, which may be launched by the insider manager, and the traditional paring computation is not necessary. Our experiment simulation shows that the similarity error between ciphertext and plaintext is tiny when the parameters are set properly. Compared to previous works, our scheme not only achieves the classification of similar encrypted EMRs but is also more efficient than traditional PKEETs since our construction does not need paring computation anymore. © 2013 IEEE.

关键词： Ciphertext

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UniSleepPos: Sleep Posture Identification System Utilizing Millimeter-wave Radar

UniSleepPos: Sleep Posture Identification System Utilizing M...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Kun Li, Min He, Chu Tian, Xu Mao, Junbin Zeng, Min Liu, Jin Xinjiang University Xinjiang Engineering Research Center of Big Data and Intelligent Software School of Software Urumqi China Central South University Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Changsha China

ISBN: (纸本)9798350386226

Sleep posture identification is crucial for accurately assessing sleep quality and diagnosing related diseases. In the realm of non-intrusive sleep monitoring, non-contact technologies are becoming increasingly mainstream. Millimeter-wave radar is frequently utilized in sleep posture identification due to its high resolution, strong penetration, and excellent sensitivity. However, traditional radar-based methods for sleep posture identification often struggle with reliability when dealing with diverse individuals and complex sleep environments. To address these challenges, we propose UniSleepPos, which designs a novel dual-view fusion mechanism to integrate depression and elevation angle signals obtained from radar, thus accurately capturing the posture information of the monitored subject in three-dimensional space. Furthermore, we combine sleep posture identification with individual characteristics, utilizing existing individual labels as prior knowledge to assist in sleep posture identification. The integration of prior knowledge provides a valuable information source for the model, helping to enhance its understanding of the data and improve its performance. We collected sleep posture data from eight volunteers using millimeter-wave radar devices under various environmental conditions. Leave-one-subject-out experiments were conducted to validate the effectiveness of UniSleepPos. The results indicated that UniSleepPos significantly outperforms existing methods, demonstrating its potential for practical applications. © 2024 IEEE.

关键词： Sleep research

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CAP: Causal Air Quality Index Prediction Under Interference with Unmeasured Confounding 25

CAP: Causal Air Quality Index Prediction Under Interference ...

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34th ACM Web Conference, WWW 2025

作者： Yang, Huayi Zheng, Chunyuan Liao, Guorui Huang, Shanshan Liao, Jun Gong, Zhili Li, Haoxuan Liu, Li School of Big Data and Software Engineering Chongqing University Chongqing China School of Mathematical Sciences Peking University Beijing China Center for Data Science Peking University Beijing China

ISBN: (纸本)9798400712746

A significant challenge in air quality index (AQI) prediction is to accurately evaluate the potential outcomes after conducting interventions in pollutant factors such as industrial emissions for each enterprise. Existing methods often suffer from spurious correlations caused by unmeasured confounders and are lack of interpretability of the model, leading to sub-optimal prediction performance. This motivates us to propose a causal AQI prediction framework (CAP) that employs a structural causal model (SCM) to characterize the causal structural variability of various AQI factors for robust AQI prediction. Specifically, we employ the front-door adjustment to explicitly eliminate unmeasured confounders by intervening in industrial emissions from the target enterprise. Meanwhile, we take industrial emissions of neighboring enterprises into account when intervening in the target enterprise and simulate the dispersion of industrial emissions through a Gaussian plume model based on meteorological factors. Experiments on two real-world datasets validate the superior performance of our model on AQI prediction compared to the state-of-the-art baselines. © 2025 Copyright held by the owner/author(s).

关键词： Air quality

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A Large-Capacity Information Hiding Algorithm Based on the Fractal Carrier 4

A Large-Capacity Information Hiding Algorithm Based on the F...

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4th IEEE International Conference on Electronic Technology, Communication and Information, ICETCI 2024

作者： Tao, Xuejiao Leng, Yahong Yang, Qian Bai, Sen Chongqing Institute of Engineering School of Software Chongqing China Chongqing Institute of Engineering Office of Teaching Affairs Chongqing China Chongqing Institute of Engineering School of Big Data and Artificial Intelligence Chongqing China

ISBN: (纸本)9798350361643

Information security is a pressing concern in the era of the Internet. Within the realm of information security, significant research focus has been placed on information hiding technology. Researchers strive to enhance both the information hiding capacity and the anti-attack capability. This paper explores the concept of concealing information within fractal images. Fractal images possess a notable attribute of being affine transformed through simple IFS code parameters, thereby generating unique fractal representations. The simplicity of the transmission parameters facilitates an effortless restoration of the carrier. By employing straightforward affine or iterative parameters, the recipient can successfully extract the originally embedded carrier, subsequently unveiling the concealed information. To further augment the security of the information hiding process, an encryption strategy may be implemented to transmit sensitive data. In this scenario, the secret carrier serves as the public key, while the fractal parameter information functions as the private key, thereby intensifying the complexity of steganography analysis. Consequently, this paper introduces a high-capacity fractal carrier information hiding algorithm, which effectively embeds information within fractal images, amplifies and adapts fractal parameters based on requirements, and enhances the overall capacity of the fractal carrier. Exploiting fractal images for information hiding purposes conveys advantages such as minimized reliance on key information and exceptional concealment abilities, thus bolstering the secrecy of hidden data, expanding the storage capacity, and fortifying the resistance against steganalysis. © 2024 IEEE.

关键词： Fractals

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Secret Sharing Based Key Agreement Protocol for Body Area Networks 18th

Secret Sharing Based Key Agreement Protocol for Body Area N...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Sheng, Weihong Cai, Bin Hu, Chunqiang Li, Ruinian School of Big Data and Software Engineering Chongqing University Chongqing China Computer Science Department Bowling Green State University Bowling Green United States

ISBN: (纸本)9783031714634

In order to provide more comprehensive medical services and personalized health monitoring according to individual needs, Body Area Networks (BANs) have been extensively studied by many researchers. As BANs involve the transmission of personal private data, the security of the communication is of utmost importance. Unfortunately, existing encryption techniques cannot be directly applied with the limited power or computation ability of the sensors in BANs. An alternative direction is to use physiological signals for key agreement. However, many of the current physiological signal-based key agreement schemes either have high overheads or are vulnerable to certain security issues. In this paper, we proposed a novel secret sharing and Bloom filter based key agreement scheme that balances overhead and security for BANs. Also, we use an approximate hash table to free ourselves from the problem of dependence on the ordering of features, which has been neglected by previous researchers. We validate the security of our scheme with based on real datasets and compare overheads with other solutions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electronic health record

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A Zero-Shot Relation Extraction Approach Based on Contrast Learning 34

A Zero-Shot Relation Extraction Approach Based on Contrast L...

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34th International Conference on software engineering and Knowledge engineering, SEKE 2022

作者： Zhu, Hongyu Zeng, Jun Yang, Yu Wu, Yingbo School of Big Data & Software Engineering Chongqing University Chongqing China

ISBN: (纸本)1891706543

The most significant advantage of the unseen relation extraction is that it can recognize unlabeled relations. While Zero-Shot Learning can meet the requirements of the identification of unseen relation through relation description information without labeled datasets. However, unseen relation extraction requires an effective method in representation and generalization, which become a challenge for zero-shot learning approach. In this paper, we propose a Zero-Shot learning Relation Extraction based on Contrastive learning Model (ZRCM) to capture deep interrelation text information. We design a comparison sample generation method which can produce several instances for one input sentence and compare the distance between positive instance and negative ones, so as to improve the hidden text information mining ability. Experiments conducted on relation extraction common datasets confirmed the promotion of ZRCM compared with the existing methods. Especially, our model can improve the F1 value by up to 7% at best. When there are fewer unseen relations to predict, our model can achieve better performance. © 2022 Knowledge Systems Institute Graduate school. All rights reserved.

关键词： Extraction

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CVAS: An Interactive Visual Analytics System for Exploring COVID-19 Information on the Web 2022

CVAS: An Interactive Visual Analytics System for Exploring C...

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6th International Conference on Compute and data Analysis, ICCDA 2022

作者： Yang, Tao Zhou, Yixuan Feng, Dan Hu, Haibo School of Big Data and Software Engineering Chongqing University Chongqing China

ISBN: (纸本)9781450395472

The outbreak of COVID-19 has been a critical social event in the past two years. The pandemic has seriously affected the world. Meanwhile, various forms of data about COVD-19 emerge on the Web endlessly, such as SNS discussions, Press releases, WHO statistics, etc. It is valuable work for government departments, news media, and health organizations to integrate and analyze these pandemics-related multi-source data on the web. In this work, we propose an interactive visual analytics system as CVAS that aims at mining and analyzing multi-source data concerned with COVD-19. Having been inspired by the Sankey diagram, we developed a view elaborately. Through appropriate interactions, massive patients' mobility data can be visualized, thus showing the spread features of the pandemic in time and space more specifically. In addition, we collected more than 10,000 trending topics and nearly 10 million related comments on the SNS as Sina Weibo. We performed NLP to analyze their sentiment, identifying key events since the outbreak and the impact of the pandemic on public sentiment. Part of our work was awarded at the China visualization and visual analysis conference (ChinaVis2020) and recognized by peers. © 2022 ACM.

关键词： Visualization

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Test Case Recommendation for Blockchain Systems Based on Graph Convolutional Networks 10

Test Case Recommendation for Blockchain Systems Based on Gra...

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10th IEEE Conference on big data Security on Cloud, bigdataSecurity 2024

作者： Li, Shuyi Zhang, Pengyu Liu, Xueyang Hu, Wenhui Peking University School of Software & Microelectronics Beijing China Big data center State Grid Corporation of China Beijing China Peking University National Engineering Research Center for Software Engineering Beijing China

ISBN: (纸本)9798350389524

In the development and operation of blockchain systems, the evaluation of these systems typically necessitates the formulation and execution of test cases. Current testing practices for blockchain systems often involve the manual specification of relevant test scenarios, with testing being conducted through inquiries or the execution of tasks within the system. However, due to the relatively narrow scope of real-world applications for blockchain systems, there exists a deficiency in appropriate test case sets for the evaluation of such systems. Consequently, this paper presents a Blockchain Test Case Recommendation System based on LightGCN, a simplified Graph Convolutional Neural Network. LightGCN dispenses with traditional graph convolutional network components such as non-linear activation and feature transformations, thereby enhancing the efficiency of model training. Experiments illustrate that this system effectively supplements and enhances the blockchain system's test case set by aggregating information from domain nodes. © 2024 IEEE.

关键词： Blockchain

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Efficient Relaxed Functional Dependency Discovery with Minimal Set Cover 40

Efficient Relaxed Functional Dependency Discovery with Minim...

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40th IEEE International Conference on data engineering, ICDE 2024

作者： Ding, Xiaoou Liu, Yida Wang, Hongzhi Wang, Chen Song, Yichen Yang, Donghua Wang, Jianmin School of Computer Science and Technology Harbin Institute of Technology China National Engineering Research Center for Big Data Software EIRI Tsinghua University China

ISBN: (纸本)9798350317152

Assessing data quality through Functional Depen-dencies (FDs) is a crucial aspect of data governance. However, with the diverse range of data sources and the exponential growth in data volume, exact FDs can sometimes be impractical for real-world applications. In contrast, relaxed functional dependencies (RFDs), which allows for some flexibility in attribute value comparisons, demonstrates greater adaptability and flexibility for big data scenarios. To address the efficient discovery of RFDs, this paper proposes a novel mining method to supplement the current research gaps. By establishing a difference table for tuples, we transform the problem into a specialized minimal set covering problem. Additionally, we introduce two optimization strategies: reducing the time complexity of enumerating the left-hand side of the base RFDs to 0 (1) and decreasing the search complexity for feasible LHS attributes and threshold candidates from O(2m-l) to O(1.5m-1). We rigorously proof that our mining approach guarantees the identification of validity and minimal RFDs. Experiments on nine real-world datasets reveal that our method significantly improves efficiency compared to existing techniques. Furthermore, it uncovers more concise and higher-quality RFDs. Importantly, the RFDs extracted through our methodology exhibit better performance in downstream cleaning tasks. © 2024 IEEE.

关键词： Quality management

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Low-repair array codes based on nested structures 4

Low-repair array codes based on nested structures

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4th International Symposium on Artificial Intelligence and Intelligent Manufacturing, AIIM 2024

作者： Huang, Juan Tang, Jinghua Tang, Dan Cai, Hongliang Yu, Hongzhou School of Software Engineering Chengdu University of Information Technology Sichuan Province Engineering Technology Research Center of Support Software of lnformatization Application Sichuan Provincial Big Data Technical Service Center Chengdu China

ISBN: (纸本)9798331541729

To address the problem of low repair efficiency of single- and double-disk of localized horizontal array code, this paper retains the original advantages of horizontal array code, transforms it on the basis of RDP 2D array code, and proposes a 2D array code model based on nested structure - NRDP (Nested Rows Diagonal Parity Code). The overall layout of the NRDP code is the same as that of the RDP code. However, when deploying data columns, the NRDP code deploys the RDP code of one prime number level smaller as a whole in the data columns of the NRDP code, forming a nested relationship. The increased redundant data reduce the repair overhead and error recovery time. Through theoretical analysis, NRDP has good compiled code complexity, improves the problem of uneven reading and writing during disk repair, and reduces the total amount of data that needs to be read during single- and double-disk failures, improving the success rate of single- and double-disk repair. The experimental results show that compared with RDP, LRRDP, and DRDP, NRDP can save 7.03%-30.01% of the single-disk failure repair time and 14.79%-26.51% of the dual-disk repair time. © 2024 IEEE.

关键词： Codes (symbols)

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