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PrivBox: Privacy-Preserving Deep Packet Inspection with Dual Double-masking Obfuscated Rule Generation

IEEE Transactions on Dependable and Secure computing 2025年

作者： Wu, Pengfei Ning, Jianting Huang, Xinyi Chen, Rongmao Zhang, Kai Liang, Kaitai Wuhan University School of Cyber Science and Engineering Wuhan China Singapore Management University School of Computing and Information Systems Singapore City University of Macau Faculty of Data Science Macau China Fujian Normal University College of Computer and Cyber Security Fuzhou China Jinan University College of Cyber Security Guangzhou China National University of Defense Technology College of Computer Changsha China Shanghai University of Electric Power College of Computer Science and Technology Shanghai China Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Netherlands

Many network middleboxes have been deployed to perform deep packet inspection (DPI) over packet payloads. However, such middleboxes cannot accomplish their tasks when the traffic is encrypted. BlindBox (SIGCOMM 2015) provided the first solution for performing DPI over encrypted traffic. To improve its efficiency, a later proposal PrivDPI (CCS 2019) introduced a practical technique to generate encrypted rules. However, a recent proposal P2DPI (ASIACCS 2021) showed that the rule generator in PrivDPI can comprise the user's privacy. In this paper, we present a new attack on P2DPI and show that the privacy of its endpoints can still be compromised by the rule generator. We comprehensively analyze the vulnerability of prior studies and present PrivBox, a new DPI system that achieves the same privacy guarantee as BlindBox while maintaining practical efficiency. This is based on a new technique called dual double-masking obfuscated rule generation. For a ruleset of 3,000, PrivBox achieves connection establishment time on the endpoint side comparable to PrivDPI and supports up to 4,672 token encryptions per second, which is sufficient for a number of real-world applications. Overall, our experiment demonstrates that PrivBox is practical and well-suited for short, frequently established sessions, especially when token repeating is common. ©2004-2012 IEEE.

关键词： Differential privacy

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FedDWA: Personalized Federated Learning with Dynamic Weight Adjustment

arXiv

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arXiv 2023年

作者： Liu, Jiahao Wu, Jiang Chen, Jinyu Hu, Miao Zhou, Yipeng Wu, Di School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China School of Computing Faculty of Science and Engineering Macquarie University Sydney Australia

Different from conventional federated learning, personalized federated learning (PFL) is able to train a customized model for each individual client according to its unique requirement. The mainstream approach is to adopt a kind of weighted aggregation method to generate personalized models, in which weights are determined by the loss value or model parameters among different clients. However, such kinds of methods require clients to download others’ models. It not only sheer increases communication traffic but also potentially infringes data privacy. In this paper, we propose a new PFL algorithm called FedDWA (Federated Learning with Dynamic Weight Adjustment) to address the above problem, which leverages the parameter server (PS) to compute personalized aggregation weights based on collected models from clients. In this way, FedDWA can capture similarities between clients with much less communication overhead. More specifically, we formulate the PFL problem as an optimization problem by minimizing the distance between personalized models and guidance models, so as to customize aggregation weights for each client. Guidance models are obtained by the local one-step ahead adaptation on individual clients. Finally, we conduct extensive experiments using five real datasets and the results demonstrate that FedDWA can significantly reduce the communication traffic and achieve much higher model accuracy than the state-of-the-art approaches. Copyright © 2023, The Authors. All rights reserved.

关键词： data privacy

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Multi-Modal data Fusion Approach Using the Structured and Sparse Principal Component Analysis (MMSSCA)

Multi-Modal Data Fusion Approach Using the Structured and Sp...

引用

Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario (ICPSITIAGS), IEEE International Conference on

作者： Jyoti Kandpal A Jonitha Janaki Sivakumar Rejon Kumar Ray School of Computing Graphic Era Hill University Dehradun Uttarakhand India Faculty of Data Science & Computing Universiti Malaysia Kelantan Malaysia Computer Science and Creative Technologies Global College of Engineering and Technology Muscat Oman Department of Business analytics Gannon University Erie PA USA

ISBN: (数字)9798350329773

ISBN: (纸本)9798350329780

There are many different sorts of data that can be gathered and analyzed, including pictures, videos, texts, speeches, music, and other noises, Video content, for example, generally includes minimum some types of audible sounds or any type of visual image, and often also useful text data that can be of some specific type of titles or subtitles, which may consist of some unique type text language which is different type of speech. “Modality” is basically defines each different type of acquisition strategy. As natural phenomena tend to be quite complex, it is unusual for a single modality to provide exhaustive understanding of the phenomenon of interest. The process of combining and interpreting the useful data between the same modal or in different modal, and also interaction with the human beings with multi-modal data fusion &the accurate and deep interpretation and understanding of data obtained from the combining process from it, are central to the research challenge on multimodal data analysis. We contend that many of these challenges are shared by researchers in different fields. Both the “why” and the “how” of data fusion are addressed in this paper. Various examples from the realms of science and technology serve as inspiration for the first problem, which is then suggested by a various types of mathematical based model framework demonstrating the utility of data fusion. To solve the second problem, we introduce the idea of “diversity” as a central concept and discuss several data-driven solutions based on matrix and tensor decompositions, highlighting how they take into account diversity in the underlying data sets to address the aforementioned issues, this research suggests a novel approach for modeling multi data fusion implemented using sparse technique and structural analysis.

关键词： Visualization Tensors Diversity reception Noise data integration Music data models

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A Trusted Lesion-assessment Network for Interpretable Diagnosis of Coronary Artery Disease in Coronary CT Angiography 39

A Trusted Lesion-assessment Network for Interpretable Diagno...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Ma, Xinghua Fang, Xinyan Zou, Mingye Luo, Gongning Wang, Wei Wang, Kuanquan Qiu, Zhaowen Gao, Xin Li, Shuo Faculty of Computing Harbin Institute of Technology Harbin China Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal Saudi Arabia Faculty of Computing Harbin Institute of Technology Shenzhen China College of Computer and Control Engineering Northeast Forestry University Harbin China Department of Computer and Data Science Case Western Reserve University Cleveland United States Department of Biomedical Engineering Case Western Reserve University Cleveland United States

ISBN: (纸本)157735897X

Coronary Artery Disease (CAD) poses a significant threat to cardiovascular patients worldwide, underscoring the critical importance of automated CAD diagnostic technologies in clinical practice. Previous technologies for lesion assessment in Coronary CT Angiography (CCTA) images have been insufficient in terms of interpretability, resulting in solutions that lack clinical reliability in both network architecture and prediction outcomes, even when diagnoses are accurate. To address the limitation of interpretability, we introduce the Trusted Lesion-Assessment Network (TLA-Net), which provides a clinically reliable solution for multi-view CAD diagnosis: (1) The causality-informed evidence collection constructs a causal graph for the diagnostic process and implements causal interventions, preventing confounders’ interference and enhancing the transparency of the network architecture. (2) The clinically-aligned uncertainty integration hierarchically combines Dirichlet distributions from various views based on clinical priors, offering confidence coefficients for prediction outcomes that align with physicians’ image analysis procedures. Experimental results on a dataset of 2,618 lesions demonstrate that TLA-Net, supported by its interpretable methodological design, exhibits superior performance with outstanding generalization, domain adaptability, and robustness. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词：

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BARA: Efficient Incentive Mechanism with Online Reward Budget Allocation in Cross-Silo Federated Learning

arXiv

引用

arXiv 2023年

作者： Yang, Yunchao Zhou, Yipeng Hu, Miao Wu, Di Sheng, Quan Z. School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China School of Computing Faculty of Science and Engineering Macquarie University Sydney Australia

Federated learning (FL) is a prospective distributed machine learning framework that can preserve data privacy. In particular, cross-silo FL can complete model training by making isolated data islands of different organizations collaborate with a parameter server (PS) via exchanging model parameters for multiple communication rounds. In cross-silo FL, an incentive mechanism is indispensable for motivating data owners to contribute their models to FL training. However, how to allocate the reward budget among different rounds is an essential but complicated problem largely overlooked by existing works. The challenge of this problem lies in the opaque feedback between reward budget allocation and model utility improvement of FL, making the optimal reward budget allocation complicated. To address this problem, we design an online reward budget allocation algorithm using Bayesian optimization named BARA (Budget Allocation for Reverse Auction). Specifically, BARA can model the complicated relationship between reward budget allocation and final model accuracy in FL based on historical training records so that the reward budget allocated to each communication round is dynamically optimized so as to maximize the final model utility. We further incorporate the BARA algorithm into reverse auction-based incentive mechanisms to illustrate its effectiveness. Extensive experiments are conducted on real datasets to demonstrate that BARA significantly outperforms competitive baselines by improving model utility with the same amount of reward budget. Copyright © 2023, The Authors. All rights reserved.

关键词： data privacy

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Competitive Facility Location with Market Expansion and Customer-centric Objective

arXiv

引用

arXiv 2024年

作者： Le, Cuong Mai, Tien Duong, Ngan Ha Ha, Minh Hoang School of Computing and Information Systems Singapore Management University Singapore Interdisciplinary Centre for Security Reliability and Trust University of Luxembourg Luxembourg SLSCM and CADA Faculty of Data Science and Artificial Intelligence College of Technology National Economics University Hanoi Viet Nam

We study a competitive facility location problem, where customer behavior is modeled and predicted using a discrete choice random utility model. The goal is to strategically place new facilities to maximize the overall captured customer demand in a competitive marketplace. In this work, we introduce two novel considerations. First, the total customer demand in the market is not fixed but is modeled as an increasing function of the customers’ total utilities. Second, we incorporate a new term into the objective function, aiming to balance the firm’s benefits and customer satisfaction. Our new formulation exhibits a highly nonlinear structure and is not directly solved by existing approaches. To address this, we first demonstrate that, under a concave market expansion function, the objective function is concave and submodular, allowing for a (1 − 1/e) approximation solution by a simple polynomial-time greedy algorithm. We then develop a new method, called Inner-approximation, which enables us to approximate the mixed-integer nonlinear problem (MINLP), with arbitrary precision, by an MILP without introducing additional integer variables. We further demonstrate that our inner-approximation method consistently yields lower approximations than the outer-approximation methods typically used in the literature. Moreover, we extend our settings by considering a general (non-concave) market-expansion function and show that the Inner-approximation mechanism enables us to approximate the resulting MINLP, with arbitrary precision, by an MILP. To further enhance this MILP, we show how to significantly reduce the number of additional binary variables by leveraging concave areas of the objective function. Extensive experiments demonstrate the efficiency of our approaches. Copyright © 2024, The Authors. All rights reserved.

关键词： Sales

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Editorial: Synopsis of Core Concepts for Explainable-AI-Ready data and Models

Editorial: Synopsis of Core Concepts for Explainable-AI-Re...

引用

International Workshop on Designing the Conceptual Landscape for a XAIR Validation Infrastructure, DCLXVI 2024

作者： Horsch, Martin Thomas Scholze, Sebastian Al Machot, Fadi Department of Data Science Faculty of Science and Technology Norwegian University of Life Sciences P.O. Box 5003 Ås1432 Norway Scientific Computing Department UKRI Science and Technology Facilities Council Daresbury Laboratory Keckwick Ln WarringtonWA4 4AD United Kingdom ATB – Institut für angewandte Systemtechnik Bremen GmbH Wiener Str. 1 Bremen28359 Germany

ISBN: (纸本)9783031892738

Reliable AI technology necessitates the development of robust validation infrastructures and data documentation schemas to ensure that AI systems become trustworthy and transparent. The International Workshop on Designing the Conceptual Landscape for a XAIR Validation Infrastructure (DCLXVI 2024), held on 11th December 2024 in Kaiserslautern, Germany, surveyed the conceptual landscape of explainable-AI-ready (XAIR) models and data. The definition of concepts relevant to data integrity, algorithmic transparency, and user interpretability were explored, resulting in a synopsis of core concepts for "XAIR." © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Applied ontology explainable-AI-readiness FAIR data

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The effect of data Augmentation Using SMOTE: Diabetes Prediction by Machine Learning Techniques 23

The effect of Data Augmentation Using SMOTE: Diabetes Predic...

引用

Proceedings of the 2023 6th Artificial Intelligence and Cloud computing Conference

作者： A. Al-Qerem A. M Ali M. Alauthman M. Al Khaldy A. Aldweesh Computer Science Department Faculty of Information Technology Zarqa University Jordan Communications and Computer Engineering Department Faculty of Engineering Al-Ahliyya Amman University Jordan Department of Information Security Faculty of Information Technology University of Petra Jordan Business Inteligence & Data Analytics University of Petra Jordan College of Computing and Information Technology Shaqra University Saudi Arabia

ISBN: (纸本)9798400716225

Diabetes mellitus, a severe and enduring condition characterized by impaired glucose metabolism, poses a substantial threat to public health. Its pervasive impact continues to escalate globally, with a rising incidence that challenges preventive measures. Despite earnest efforts, individuals struggle to evade the clutches of diabetes, necessitating innovative approaches for disease management. Traditional methodologies in diabetes health monitoring exhibit limitations, prompting the exploration of advanced techniques. This study employs machine learning (ML) methods to delve into diabetes, aiming to enhance diagnostic accuracy. The primary objective is to develop a method capable of precise diabetes diagnoses with a heightened level of precision. The investigation incorporates machine learning algorithms, specifically Random Forest (RF), K Nearest Neighbor (KNN), and Logistic Regression. The inclusion of these algorithms seeks to streamline data processing ***, this study incorporates the Synthetic Minority Over-sampling Technique (SMOTE) as a data augmentation strategy. SMOTE addresses imbalances in the dataset, contributing to a more robust and representative sample. The research evaluates the effectiveness and accuracy of diabetes prediction using these algorithms both before and after SMOTE implementation. By considering the impact of SMOTE, the study aims to determine the optimal algorithm for assessing diabetes development. The comparative analysis sheds light on how SMOTE enhances the overall performance of machine learning models. This nuanced approach not only refines diabetes diagnostic protocols but also underscores the significance of addressing data imbalances in predictive modeling for enhanced precision in disease prediction.

关键词： Classification data augmentation Diabetes

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Deep Reinforcement Learning for Energy Efficiency Maximization in RSMA-IRS-Assisted ISAC System

arXiv

引用

arXiv 2025年

作者： Ma, Zhangfeng Zhang, Ruichen Ai, Bo Lian, Zhuxian Zeng, Linzhou Niyato, Dusit School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore School of Information and Communications Engineering Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an710049 China

This paper proposes a three-dimensional (3D) geometry-based channel model to accurately represent intelligent reflecting surfaces (IRS)-enhanced integrated sensing and communication (ISAC) networks using rate-splitting multiple access (RSMA) in practical urban environments. Based on this model, we formulate an energy efficiency (EE) maximization problem that incorporates transceiver beamforming constraints, IRS phase adjustments, and quality-of-service (QoS) requirements to optimize communication and sensing functions. To solve this problem, we use the proximal policy optimization (PPO) algorithm within a deep reinforcement learning (DRL) framework. Our numerical results confirm the effectiveness of the proposed method in improving EE and satisfying QoS requirements. Additionally, we observe that system EE drops at higher frequencies, especially under double-Rayleigh fading. © 2025, CC BY.

关键词： Deep reinforcement learning

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Large Language Models for Sexual, Reproductive, and Maternal Health Rights*

Large Language Models for Sexual, Reproductive, and Maternal...

引用

IEEE International Conference on Healthcare Informatics (ICHI)

作者： Walelign Sewunetie Assefa Beza Hailemariam Abebe Tesfamariam M Abuhay Wasyihun Admass Hayat Hassen Tsion Haile Hana Hailemariam Lydia Debebe Nurlign Moges Nathnael Bekele Surafel L Tilahun Mahlet Berta Mahlet Mammo Seid Muhie Yimam Kovacs Laszlo School of Computing Debre Markos University Ethiopia Faculty of Computing Bahir Dar University Ethiopia University of Gondar Ethiopia Queen's University Canada School of Computing Mekdela Amba University Mekdela Ethiopia College of Medicine and Health Sciences University of Gondar Ethiopia Saint Paul's Hospital Millennium Medical College AA Ethiopia College of Medicine and Health Sciences Bahir Dar University Ethiopia College of Medicine and Health Sciences Debre Markos University Ethiopia Addis Ababa Science and Technology University Ethiopia HPC & Big Data Analytics CoE Addis Ababa Science and Technology University Ethiopia House of Computing and Data Science University of Hamburg Hamburg Germany Institute of Informatics Univerisity of Miskolc Miskolc Hungary

ISBN: (数字)9798350383737

ISBN: (纸本)9798350383744

This research explores the potential of Large Language Models in the context of healthcare solutions, with a specific focus on Sexual, Reproduc-tive, and Maternal Health Rights (SRMHR) Question Answering (QA) in the low-resource language, Amharic (““'Ie;;:). To construct the dataset, we first collected data from medical textbooks and guidelines authored by reputable medical institutions and organizations. U ti-lizing automatic question-and-answer generation techniques, we then generated pairs for the dataset. Sub-sequently, the dataset underwent annotation, translation, and evaluation processes, resulting in a refined collection of 2.8k Amharic datasets. We use the dataset to fine-tune the LLaMA-2-Amharic model, with test results assessed using BLEU scores and human-level evaluations, demonstrating promising outcomes. The curated Amharic SRMHRQA dataset serves as a foun- dational resource for future research. However, further enhancements are necessary to optimize its efficacy, particularly within the realm of SRMHR for low- resource languages like Amharic. Future research could involve scaling up the dataset in terms of size, quality, and domain coverage.

关键词： Annotations Large language models Medical services Organizations Question answering (information retrieval) Informatics Guidelines

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