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Blockchain-based secure and verifiable storage scheme for IPFS-assisted IoT with homomorphic encryption

Computing 2025年第7期107卷 1-33页

作者： Zhang, Shiwen Zhang, Wen Liang, Wei Li, Kuanching Dobre, Ciprian School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Sanya Research Institute Hunan University of Science and Technology Sanya China National University of Science and Technology Politehnica Bucharest Romania & National Institute for Research and Development in Informatics Bucharest Romania

The Internet of Things (IoT) devices are increasingly permeating our daily lives and various industries, but their limited storage capacity poses significant challenges. The InterPlanetary File System (IPFS) is an ideal platform that enables decentralized data storage and sharing, providing content hashes for download and file sharing. However, IPFS lacks robust security mechanisms, making it vulnerable to data tampering, leakage, and unauthorized access. In particular, devices lose control over their data when sharing file hashes with arbitrary devices, enabling unauthorized file sharing without the device’s consent. To address these challenges, we propose a blockchain-based secure and verifiable storage scheme for IPFS-assisted IoT with homomorphic encryption, enhancing security and access control, thereby differentiating it from previous approaches. Specifically, our scheme introduces: (1) an improved Paillier-based homomorphic encryption mechanism that not only ensures data is transmitted on the blockchain in ciphertext rather than plaintext, enhancing privacy protection, but also optimizes computational performance, improving the efficiency of encryption and decryption processes; (2) a novel distributed key management framework that generates random encryption keys to resist brute-force attacks and eliminate single points of failure; (3) a digital signature algorithm for verifying private data, guaranteeing data integrity and authenticity, while preventing unauthorized modifications. Furthermore, our scheme differs from existing solutions by integrating these security enhancements into a unified framework, ensuring stronger protection for IoT data in a decentralized environment. Formal security analysis and performance evaluations confirm the superiority of our method in terms of security, efficiency, and practicality.

关键词： Blockchain Distributed storage Internet of Things (IoT) Verifiable

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Bottleneck-minimal indexing for generative document retrieval 24

Bottleneck-minimal indexing for generative document retrieva...

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Proceedings of the 41st International Conference on Machine Learning

作者： Xin Du Lixin Xiu Kumiko Tanaka-Ishii Waseda Research Institute for Science and Engineering Waseda University Department of Mathematical Informatics The University of Tokyo Department of Computer Science and Engineering Waseda University

We apply an information-theoretic perspective to reconsider generative document retrieval (GDR), in which a document x ∈ Χ is indexed by t ∈ Τ, and a neural autoregressive model is trained to map queries Q to Τ. GDR can be considered to involve information transmission from documents X to queries Q, with the requirement to transmit more bits via the indexes Τ. By applying Shannon's rate-distortion theory, the optimality of indexing can be analyzed in terms of the mutual information, and the design of the indexes Τ can then be regarded as a bottleneck in GDR. After reformulating GDR from this perspective, we empirically quantify the bottleneck underlying GDR. Finally, using the NQ320K and MARCO datasets, we evaluate our proposed bottleneck-minimal indexing method in comparison with various previous indexing methods, and we show that it outperforms those methods.

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Using Machine Learning to Identify and Categorize Personally Identifiable Information and Payment Card Industry Data in Textual Content

Using Machine Learning to Identify and Categorize Personally...

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Advanced research in Computing (ICARC), International Conference on

作者： Milinda Arambawela Achala Aponso Computer Science and Engineering University of Westminster London UK Department of Computing Informatics Institute of Technology Colombo Sri Lanka

The advent of the Internet has significantly stream-lined daily tasks through the rapid increase of online services. Everyday activities, such as purchasing goods and scheduling appointments with healthcare professionals, have become more speedy, efficient and user-friendly with the integration of the Internet. The continuous improvement of online services has led to many people moving towards digital activities. As a result, it has heightened the recording of personal and payment transaction data across various storage mediums, including databases and log files. The protection and regulation of this sensitive data are imperative, aligning with the guidelines outlined in GDPR and PCI-DSS compliances. Recognizing exposed personal data poses a considerable challenge. This research introduces a novel approach to identifying payment card industry data (PCI) and personally identifiable information (PII). The research project proposes a machine learning-based text classification model utilizing the Convolutional Neural Network (CNN) model to discern PII and PCI data within a given text. The CNN model has been constructed and compared against Naive Bayes, Gradient Boost, Random Forest, and Support Vector Machine (SVM) models. The CNN model achieved the highest accuracy at 0.96 (96%). Additionally, the F1 scores for each class were significant, with PII scoring 0.94, PCI scoring 0.95, and Normal scoring 0.99. Following the model's construction and training, it was employed with the saved tokenizer's word indexes and label encoders in the developed classification tool. This tool successfully delivered the promised results, identifying exposed PII and PCI data.

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KnowSOntoWSR: Web Service Recommendation System Using Semantically Driven QoS Ontology-Based Knowledge-Centred Paradigm 1

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7th International Conference on Internet of Things and Connected Technologies, ICIoTCT 2022

作者： Dhanvardini, R. Deepak, Gerard Priyadarshini, J. Sheeba Santhanavijayan, A. Healthcare Informatics Division Optum UnitedHealth Groups Hyderabad India Department of Computer Science Engineering National Institute of Technology Tiruchirappalli India Department of Data Science Manipal Institute of Technology Bengaluru Bengaluru India Department of Data Science Manipal Academy of Higher Education Manipal India Bangalore India

ISBN: (数字)9789811997198

ISBN: (纸本)9789811997181

Web services have significantly expanded and become a key enabling technology for online data, application and resource sharing. Designing new methods for efficient and reliable web service recommendation has been of tremendous importance with the growing usage and prominence of web services. It would be ideal for a system to suggest online services that are in line with consumers’ preferences without requesting specific query information from them. Quality of Service (QoS) is vital for characterising non-functional aspects of Web services as they become more prevalent and widely used on the World Wide Web. The KnowSOntoWSR framework, which is built on a knowledge-driven and semantically inclined model that adheres to QoS ontology, is proposed in this research. AWS and WebSphere are employed as knowledge tags, and the powerful machine learning classifier XGBoost is applied. The features and recommendations are computed using the Twitter semantic similarity. The proposed framework outperforms the baseline models’ estimates with an accuracy of 95.94% and average F-measure of 95.93%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Quality of service

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Editorial:Special Topic on Federated Learning for IoT and Edge Computing

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ZTE Communications 2022年第3期20卷 1-2页

作者： PAN Yi CUI Laizhong CAI Zhipeng LI Wei Faculty of Computer Science and Control Engineering at Shenzhen Institute of Advanced Technology Chinese Academy of SciencesChina the Academy of the United Nations Sciences and Technology Organization Ukrainian Academy of Engineering Sciences American Institute for Medical and Biological Engineering Institute of Engineering Technology the College of Computer Science and Software Engineering at Shenzhen University China Department of Computer Science and the College of Business at Georgia State University USA the Center for Distributed and High Performance Computing School of Computer ScienceThe University of Sydney the Australia-China Joint Research Center on Energy Informatics and Demand Response Technologies

Recent years have witnessed the proliferation of Internet of Things(IoT),in which billions of devices are connected to the Internet,generating an overwhelming amount of *** is challenging and infeasible to transfer an... 详细信息

关键词： cloud generating data.

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Public key-based search on encrypted data using blockchain and non-blockchain enabled cloud storage: a comprehensive survey, analysis and future research scopes

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Cluster Computing 2025年第6期28卷 1-43页

作者： Mallick, Debjani Das, Ashok Kumar Varri, Uma Sankararao Park, Youngho Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad India Department of Computer Science and Engineering College of Informatics Korea University Seoul South Korea Computer Science and Engineering SRM University AP Amaravati India School of Electronic and Electrical Engineering Kyungpook National University Daegu South Korea

Cloud-based storage service has emerged as a promising alternative to managing local storage, offering users additional features, such as storage, backup, and restoration of various resources, including software, applications, and sensitive private information, within a virtual database, while ensuring data confidentiality and security. However, the widespread use of cloud services by individuals and organisations raises concerns regarding user accessibility and data security due to increasing social threats and adversarial attacks. Searchable encryption (SE) has been introduced to address these issues, providing a secure environment for a convenient and effective way of data searching and sharing. SE models have been advanced by integrating blockchain functionalities to tackle certain existing vulnerabilities and enhance user accessibility, data privacy, and integrity. This survey work explores various research works on SE techniques leveraging cloud and blockchain functionalities, discussing and categorizing the diverse approaches based on different criteria. This study also discusses the different enhancements made to SE techniques over the years, including the underlying requirements that led to the inclusion of blockchain functionalities. Moreover, this study provides a comparative analysis of existing survey works done in this area, which highlights a lack of recent literature surveys that thoroughly explore blockchain-based public key encryption with keyword search (PEKS) schemes. Particularly, we delve into the technical aspects of PEKS by classifying, analyzing and comparing different functionalities based on various aspects. The survey concludes with a comparative analysis of existing PEKS solutions and a discussion on identified research gaps, aiming to improve future research on PEKS approaches in this emerging field.

关键词： Searchable encryption Public key cryptosystem Blockchain Cloud storage Security

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A Recurrent Neural Network for Image Deblocking Detection and Quality Enhancement

A Recurrent Neural Network for Image Deblocking Detection an...

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International Conference on Smart Systems and Inventive technology (ICSSIT)

作者： R Venkatesan A. Pandiaraj M Selvakumar Department of Information Technology Bannari Amman Institute of Technology–Sathyamangalam Department of Computer Science and Engineering School of Computing Kalasalingam Academic of Research and Education-Srivilliputhur

With the advancement of intelligent media, it is becoming much easier to control and adjust progressed video without leaving any visual snippets of data. Since video pressure is so prevalent in modern accounts, the use of deblocking systems to conceal the video adjusting may be a viable alternative. Modified JPEG (MJPEG) is a well-known video format in which each video outline or wound around the field of an electronicvideo gatheringis squeezed independently as a JPEG image. The change may use strong sight and sound deblocking techniques to cover the video changing follows by isolating the MJPEG video into JPEG picture follows. This research study proposes a novel approach to perceive deblocking, which can consequently learn portrayals that are dependent upon a critical learning structure. Initially Recurrent Neural Network (RNN) is trained based on the available datasets to become familiar with the features of deblocking techniques.. Some parts of the images are eliminated with the RNN by applying a proper assessed sliding-window to review the entire image. In order to secure the final discriminative component, a close-by pooling technique is used to set the created image representation.

关键词： Visualization Image segmentation Recurrent neural networks Transform coding Wounds Media Image representation

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MassSpecGym: A benchmark for the discovery and identification of molecules 38

MassSpecGym: A benchmark for the discovery and identificatio...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Bushuiev, Roman Bushuiev, Anton de Jonge, Niek F. Young, Adamo Kretschmer, Fleming Samusevich, Raman Heirman, Janne Wang, Fei Zhang, Luke Dührkop, Kai Ludwig, Marcus Haupt, Nils A. Kalia, Apurva Brungs, Corinna Schmid, Robin Greiner, Russell Wang, Bo Wishart, David S. Liu, Li-Ping Rousu, Juho Bittremieux, Wout Rost, Hannes Mak, Tytus D. Hassoun, Soha Huber, Florian van der Hooft, Justin J.J. Stravs, Michael A. Böcker, Sebastian Sivic, Josef Pluskal, Tomáš Institute of Organic Chemistry and Biochemistry The Czech Academy of Sciences Czech Republic Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Czech Republic Bioinformatics Group Wageningen University & Research Netherlands Department of Computer Science University of Toronto Canada Bioinformatics Institute for Computer Science Friedrich Schiller University Jena Germany Department of Computer Science University of Antwerp Belgium Department of computing science University of Alberta Canada Alberta Machine Intelligence Institute Canada Department of Molecular Genetics University of Toronto Canada Bright Giant GmbH Department of Computer Science Tufts University United States Department of Biological Sciences University of Alberta Canada Department of Computer Science Aalto University Finland Mass Spectrometry Data Center National Institute of Standards and Technology United States Department of Chemical and Biological Engineering Tufts University United States Centre for Digitalisation and Digitality University of Applied Sciences Düsseldorf Germany Department of Biochemistry University of Johannesburg South Africa Eawag Swiss Federal Institute of Aquatic Science and Technology Switzerland

The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure from its mass spectrum is exceptionally challenging, even when performed by human experts. As a result, the vast majority of acquired MS/MS spectra remain uninterpreted, thereby limiting our understanding of the underlying (bio)chemical processes. Despite decades of progress in machine learning applications for predicting molecular structures from MS/MS spectra, the development of new methods is severely hindered by the lack of standard datasets and evaluation protocols. To address this problem, we propose MassSpecGym - the first comprehensive benchmark for the discovery and identification of molecules from MS/MS data. Our benchmark comprises the largest publicly available collection of high-quality labeled MS/MS spectra and defines three MS/MS annotation challenges: de novo molecular structure generation, molecule retrieval, and spectrum simulation. It includes new evaluation metrics and a generalization-demanding data split, therefore standardizing the MS/MS annotation tasks and rendering the problem accessible to the broad machine learning community. MassSpecGym is publicly available at https://***/pluskal-lab/MassSpecGym. © 2024 Neural information processing systems foundation. All rights reserved.

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Natural Language Processing for Generating Synthetic News Detection 7

Natural Language Processing for Generating Synthetic News De...

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7th International Conference on Contemporary Computing and informatics, IC3I 2024

作者： Sharan, Bediga Mulani, Umar Mubarak Naveenkumar, R. Badodia, Sujit Kumar Makawana, Hemant Dhanraj, Joshuva Arockia Sr University School of Computer Science & Artificial Intelligence Telangana Warangal India Kj College of Engineering and Management Research Maharashtra Pune India Chandigarh Colleges of Engineering Chandigarh Group of Colleges Department of Computer Science and Engineering Jhanjeri Punjab Mohali India Institute of Engineering and Technology Devi Ahilya Vishvavidyalaya Madhya Pradesh Indore India Devarakaggalahalli Karnataka Bengaluru India

ISBN: (纸本)9798350350067

Synthetic news production, language translation, and text summarization have all seen significant success with sophisticated text generating techniques. Although neural model-generated false news detection has advanced recently, the results are not easily transferable to the efficient identification of misinformation created by humans. The significant contrasts between fake news produced by machines and those written by humans, including style and underlying intentions, are what prevents them from successfully transferring. In light of this, we provide a brand-new framework for creating training cases that draw inspiration from the well-known techniques and styles of propaganda written by people. But these methods might be misused to spread false information. researchers create superior news material using a unique generation technique called FACTGEN in order to gain a greater understanding the possible risks associated with synthetic news. Because most text generation techniques now in use either provide very little more information or break continuity across the inputs and outputs, the synthetic news becomes less reliable. The results of experiments conducted on real-world datasets show that FACTGEN generates news items that are bursting with data and accurate. © 2024 IEEE.

关键词： Translation (languages)

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Study of Disease Identification in Pomegranate Using Leaf Detection Technique 4

Study of Disease Identification in Pomegranate Using Leaf De...

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4th IEEE International Conference on Emerging Smart Computing and informatics, ESCI 2022

作者： Wakhare, Prashant B. Neduncheliyan, S. Thakur, Kshitij R. Computer Science and Engineering Bharath Institute of Higher Education and Research Tamil Nadu India All India Shri Shivaji Memorial Society's Institute of Information Technology Department of Computer Engineering Maharashtra India

ISBN: (纸本)9781665400732

Internet of Things (IoT) and Artificial Intelligence (AI) powered by 5G has opened new doors of technology and innovations in the field of agriculture. There is a vast scope of application and development of the above mentioned technologies to detect any shortcomings that may lead to heavy losses for farmer. This paper surveys the use of Internet of Things (IoT) to implement leaf detection on pomegranate plant. It also surveys the use of Artificial Intelligence (AI) to analyze the data collected from leaf detection and identify the diseases that may affect the pomegranate yield. © 2022 IEEE.

关键词： Internet of things

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