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Zero-knowledge Proof-based Practical Federated Learning on Blockchain

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arXiv 2023年

作者： Xing, Zhibo Zhang, Zijian Li, Meng Liu, Jiamou Zhu, Liehuang Russello, Giovanni Asghar, Muhammad Rizwan The School of Computer Science The University of Auckland Auckland New Zealand The School of Cyberspace Science and Technology Beijing Institute of Technology Beijing100081 China Southeast Institute of Information Technology Beijing Institute of Technology Fujian351100 China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Anhui Hefei230601 China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology China The Department of Computer Science University of Surrey The School of Computer Science The University of Auckland Auckland New Zealand

Since the concern of privacy leakage extremely discourages user participation in sharing data, federated learning has gradually become a promising technique for both academia and industry for achieving collaborative learning without leaking information about the local data. Unfortunately, most federated learning solutions cannot efficiently verify the execution of each participant's local machine learning model and protect the privacy of user data, simultaneously. In this article, we first propose a Zero-knowledge Proof-based Federated Learning (ZKP-FL) scheme on blockchain. It leverages zero-knowledge proof for both the computation of local data and the aggregation of local model parameters, aiming to verify the computation process without requiring the plaintext of the local data. We further propose a Practical ZKP-FL (PZKP-FL) scheme to support fraction and non-linear operations. Specifically, we explore a Fraction-Integer mapping function, and use Taylor expansion to efficiently handle non-linear operations while maintaining the accuracy of the federated learning model. We also analyze the security of PZKP-FL. Performance analysis demonstrates that the whole running time of the PZKP-FL scheme is approximately less than one minute in parallel execution. Copyright © 2023, The Authors. All rights reserved.

关键词： Blockchain

Assessing the Impact of Text Preprocessing in Sentiment Analysis of Short Social Network Messages in the Russian Language

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Assessing the Impact of Text Preprocessing in Sentiment Anal...

2020 International Conference on data Analytics for Business and Industry: Way Towards a Sustainable Economy, ICDABI 2020

作者： Araslanov, Egor Komotskiy, Evgeniy Agbozo, Ebenezer Ural Federal University Engineering School of Information Technologies Telecommunications and Control Systems Yekaterinburg Russia Ural Federal University Department of Big Data Analysis and Methods of Videoanalysis Yekaterinburg Russia

ISBN: (纸本)9781728196756

Presently, social networks provide individuals with a unique ability to share personal opinions with others across the world. On one of such mediums, Twitter, personal opinions and general information are presented in the form of short texts (with a limit of 280 characters). Machine learning algorithms could be leveraged in detecting sentiments and opinions within such textual data. For more efficient results, text is usually transformed into a better state through the process of pre-processing. To detect the impact of this step, we used a corpus of Twitter posts in Russian language labelled either as positive or negative sentiments and applied various combinations of preprocessing techniques. Furthermore, we classified the transformed texts with a Naive Bayes Classifier and Logistic Regression and furthermore evaluated the accuracy. Results for each combination of preprocessing type and model were compiled into tables and the conclusions on impact of preprocessing with respect to the Russian language were highlighted. © 2020 IEEE.

关键词： Sentiment analysis

Aerial Active STAR-RIS-assisted Satellite-Terrestrial Covert Communications

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arXiv 2025年

作者： Zhang, Chuang Sun, Geng Li, Jiahui Wang, Jiacheng Zhang, Ruichen Niyato, Dusit Mao, Shiwen Quek, Tony Q.S. Computer Science and Technology Jilin University Changchun130012 China Singapore University of Technology and Design Singapore487372 Singapore College of Computer Science and Technology Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Department of Electrical and Computer Engineering Auburn University AuburnAL36849 United States Information System Technology and Design Pillar Singapore University of Technology and Design Singapore487372 Singapore

An integration of satellites and terrestrial networks is crucial for enhancing performance of next generation communication systems. However, the networks are hindered by the long-distance path loss and security risks in dense urban environments. In this work, we propose a satellite-terrestrial covert communication system assisted by the aerial active simultaneous transmitting and reflecting reconfigurable intelligent surface (AASTAR-RIS) to improve the channel capacity while ensuring the transmission covertness. Specifically, we first derive the minimal detection error probability (DEP) under the worst condition that the Warden has perfect channel state information (CSI). Then, we formulate an AASTAR-RIS-assisted satellite-terrestrial covert communication optimization problem (ASCCOP) to maximize the sum of the fair channel capacity for all ground users while meeting the strict covert constraint, by jointly optimizing the trajectory and active beamforming of the AASTAR-RIS. Due to the challenges posed by the complex and high-dimensional state-action spaces as well as the need for efficient exploration in dynamic environments, we propose a generative deterministic policy gradient (GDPG) algorithm, which is a generative deep reinforcement learning (DRL) method to solve the ASCCOP. Concretely, the generative diffusion model (GDM) is utilized as the policy representation of the algorithm to enhance the exploration process by generating diverse and high-quality samples through a series of denoising steps. Moreover, we incorporate an action gradient mechanism to accomplish the policy improvement of the algorithm, which refines the better state-action pairs through the gradient ascent. Simulation results demonstrate that the proposed approach significantly outperforms important benchmarks, and also validate the performance and practicality under different algorithm parameters and environment settings. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

Real or Virtual: A Video Conferencing Background Manipulation-Detection System

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arXiv 2022年

作者： Nowroozi, Ehsan Mekdad, Yassine Conti, Mauro Milani, Simone Uluagac, A. Selcuk Yanikoglu, Berrin Faculty of Engineering and Natural Sciences Sabanci University Istanbul Turkey Center of Excellence in Data Analytics Sabanci University Istanbul Turkey Department of Mathematics University of Padua Padua Italy Cyber-Physical Systems Security Lab Florida International University MiamiFL United States Department of Information Engineering University of Padua Padua Italy

Recently, the popularity and wide use of the last-generation video conferencing technologies created an exponential growth in its market size. Such technology allows participants in different geographic regions to have a virtual face-to-face meeting. Additionally, it enables users to employ a virtual background to conceal their own environment due to privacy concerns or to reduce distractions, particularly in professional settings. Nevertheless, in scenarios where the users should not hide their actual locations, they may mislead other participants by claiming their virtual background as a real one. Therefore, it is crucial to develop tools and strategies to detect the authenticity of the considered virtual background. In this paper, we present a detection strategy to distinguish between real and virtual video conferencing user backgrounds. We demonstrate that our detector is robust against two attack scenarios. The first scenario considers the case where the detector is unaware about the attacks (i.e., the forensically-edited frames are not part of the training set). In the second scenario, we make the detector aware of the adversarial attacks, which we refer to Adversarial Multimedia Forensics (i.e, the forensically-edited frames are included in the training set). Given the lack of publicly available dataset of virtual and real backgrounds for video conferencing, we created our own dataset and made them publicly available [1]. Then, we demonstrate the robustness of our detector against different adversarial attacks that the adversary considers. Ultimately, our detector's performance is significant against the CRSPAM1372 [2] features, and post-processing operations such as geometric transformations with different quality factors that the attacker may choose. Moreover, our performance results shows that we can perfectly identify a real from a virtual background with an accuracy of 99.80%. © 2022, CC BY.

关键词： Convolutional neural networks

Federated Learning-Based Intelligent Indoor Smoke and Fire Detection System for Smart Buildings

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Federated Learning-Based Intelligent Indoor Smoke and Fire D...

Telecommunications and Intelligent systems (ICTIS), International Conference on

作者： Mohamed Rafik Aymene Berkani Ammar Chouchane Yassine Himeur Anes Abdennebi Seref Sagiroglu Abbes Amira Research Laboratory in Advanced Electronics Systems (LSEA) University Yahia Fares of Medea Medea Algeria Laboratory of LI3C University Biskra University Center of Barika Barika Algeria College of Engineering and Information Technology University of Dubai Dubai United Arab Emirates Software and Information Technology Engineering École de Technologie Supérieure Montréal Canada Artificial Intelligence and Big Data Analytics Security R&D Center Gazi University Ankara Turkey Department of Computer Science University of Sharjah Sharjah United Arab Emirates

ISBN: (数字)9798331527396

ISBN: (纸本)9798331527402

Ensuring safety in smart buildings is crucial due to the increasing prevalence of smoke and fire hazards in modern environments. This paper introduces a novel privacy-preserving FL approach based on a CNN1D for smoke and fire detection in smart buildings. Our system integrates data from wearable environmental sensors to train a lightweight, edge-deployable DL-CNN1D model, ensuring data privacy while enabling collaborative learning across distributed clients using Federated Averaging (FedAvg) aggregation. Experiments conducted on a comprehensive air measurement dataset for smoke and fire detection demonstrate exceptional performance, with the global model achieving 99.97% accuracy, 99.96% precision, in smoke and fire recognition. Our model demonstrates a low communication cost of 0.4 MB, underscoring its efficiency for real-time applications. Our FL-based approach represents a significant step towards balancing the need for robust safety systems with growing privacy concerns in smart building environments.

关键词： Privacy Smart buildings Costs Federated learning Atmospheric modeling data models Convolutional neural networks Wearable sensors Monitoring Intelligent sensors

Digital Innovations Development: Causal factors and relations - A Case Study of Montenegro

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Digital Innovations Development: Causal factors and relation...

International Scientific-Professional Conference on information Technology (IT)

作者： Ivana Ognjanović Luka Laković Ramo Šendelj Laszlo Bokor Peter A. Kara Christoph Reich Manav Madan Milovan Roganović Emmanouil Zouilas John Mantas Jevto Eraković Tanja Radusinović Nada Rakočević Faculty for Information Systems and Technologies University of Donja Gorica Podgorica Montenegro Humanistic Studies University of Donja Gorica Podgorica Montenegro Department of Networked Systems and Services Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary Institute of Data Science Cloud Computing and IT Security Furtwangen University Furtwangen Germany Clinic for Neurology Clinical Centre of Montenegro Podgorica Montenegro Faculty of Nursing School of Health Sciences National and Kapodistrian University of Athens Goudi Greece Chamber of Economy of Montenegro Podgorica Montenegro

In order for economies to expand and develop globally, innovation is essential. A popular statistic for evaluating innovation between countries is the Global Innovation Index (GII), which provides a thorough analysis of the state of innovation worldwide. In this context, this paper aims to contribute to the literature by examining the multi-factoral effects of the innovation ecosystem on innovation development (internal causal effects), and the impact of global trends and crises on the innovation ecosystem (external causal effects) in Montenegro. The resources provided in this paper offer valuable insights into the innovation landscape in Montenegro and the Western Balkans, as well as the impact of globalization, global trends, and crises on innovation in the region. The paper also discusses the function of innovation hubs such as the DigNest project, in promoting cooperation between academic institutions, government agencies and corporations, and supporting the exchange of knowledge and fresh concepts.

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Ontology-based data interestingness: A state-of-the-art review

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Natural Language Processing Journal

Natural Language Processing Journal 2023年 4卷

作者： Abhilash C.B. Kavi Mahesh Department of Computer Science and Engineering Indian Institute of Information Technology Dharwad India Department of Data Science and Intelligent Systems Indian Institute of Information Technology Dharwad India

In recent years, there has been significant growth in the use of ontology-based methods to enhance data interestingness. These methods play a crucial role in knowledge management by providing explicit, shareable, and reusable knowledge descriptions. However, limited research has been conducted to explore the impact and practical benefits of employing ontologies to increase data interestingness. A comprehensive analysis of the current state-of-the-art approaches in ontology-based data interestingness is essential to identify future advancements and directions in this field. The main objective of this study is to present a clear roadmap showcasing how ontologies contribute to data interestingness and to evaluate their effectiveness in the existing literature. The findings of this research offer an integrated and comprehensive understanding of the following aspects: (1) interestingness metrics in data mining, (2) association rule mining to identify interesting patterns, (3) ontology-based methods to improve data interestingness, and (4) techniques that use ontologies to derive interestingness in data. The paper concludes with a summary of ontology-based methods for data interestingness and outlines potential avenues for future research.

关键词： Ontology data interestingness knowledge management Semantics Association rule mining data mining Interestingness metrics Ontology-based methods

Causal structure discovery from distributions arising from mixtures of dags 37

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Causal structure discovery from distributions arising from m...

37th International Conference on Machine Learning, ICML 2020

作者： Saeed, Basil Panigrahi, Snigdha Uhler, Caroline Laboratory for Information and Decision Systems Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA United States Department of Statistics University of Michigan Ann ArborMI United States Department of Biosystems Science and Engineering ETH Zurich Switzerland

ISBN: (纸本)9781713821120

We consider distributions arising from a mixture of causal models, where each model is represented by a directed acyclic graph (DAG). We provide a graphical representation of such mixture distributions and prove that this representation encodes the conditional independence relations of the mixture distribution. We then consider the problem of structure learning based on samples from such distributions. Since the mixing variable is latent, we consider causal structure discovery algorithms such as FCI that can deal with latent variables. We show that such algorithms recover a "union"of the component DAGs and can identify variables whose conditional distribution across the component DAGs vary. We demonstrate our results on synthetic and real data showing that the inferred graph identifies nodes that vary between the different mixture components. As an immediate application, we demonstrate how retrieval of this causal information can be used to cluster samples according to each mixture component. Copyright © 2020 by the Authors. All rights reserved.

关键词： Mixtures

A Review of Multimodal Explainable Artificial Intelligence: Past, Present and Future

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arXiv 2024年

作者： Sun, Shilin An, Wenbin Tian, Feng Nan, Fang Liu, Qidong Liu, Jun Shah, Nazaraf Chen, Ping School of Computer Science and Technology Xi’an Jiaotong University Xi’an710049 China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an710049 China Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an710049 China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University Xi’an710049 China Institute for Future Transport and Cities Coventry University Priory Street CoventryCV1 5FB United Kingdom Department of Engineering University of Massachusetts Boston BostonMA02125 United States

Artificial intelligence (AI) has rapidly developed through advancements in computational power and the growth of massive datasets. However, this progress has also heightened challenges in interpreting the "black-box" nature of AI models. To address these concerns, eXplainable AI (XAI) has emerged with a focus on transparency and interpretability to enhance human understanding and trust in AI decision-making processes. In the context of multimodal data fusion and complex reasoning scenarios, the proposal of Multimodal eXplainable AI (MXAI) integrates multiple modalities for prediction and explanation tasks. Meanwhile, the advent of Large Language Models (LLMs) has led to remarkable breakthroughs in natural language processing, yet their complexity has further exacerbated the issue of MXAI. To gain key insights into the development of MXAI methods and provide crucial guidance for building more transparent, fair, and trustworthy AI systems, we review the MXAI methods from a historical perspective and categorize them across four eras: traditional machine learning, deep learning, discriminative foundation models, and generative LLMs. We also review evaluation metrics and datasets used in MXAI research, concluding with a discussion of future challenges and directions. A project related to this review has been created at https://***/ShilinSun/mxai_review. Copyright © 2024, The Authors. All rights reserved.

关键词： data fusion