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Privacy-preserving explainable AI: a survey

science China(information sciences) 2025年第1期68卷 23-56页

作者： Thanh Tam NGUYEN Thanh Trung HUYNH Zhao REN Thanh Toan NGUYEN Phi Le NGUYEN Hongzhi YIN Quoc Viet Hung NGUYEN School of Information and Communication Technology Griffith University School of Computer and Communication Sciences Ecole Polytechnique Federale de Lausanne Faculty of Mathematics and Computer Science University of Bremen Faculty of Information Technology HUTECH University Department of Computer Science Hanoi University of Science and Technology School of Electrical Engineering and Computer Science The University of Queensland

As the adoption of explainable AI(XAI) continues to expand, the urgency to address its privacy implications intensifies. Despite a growing corpus of research in AI privacy and explainability, there is little attention on privacy-preserving model explanations. This article presents the first thorough survey about privacy attacks on model explanations and their countermeasures. Our contribution to this field comprises a thorough analysis of research papers with a connected taxonomy that facilitates the categorization of privacy attacks and countermeasures based on the targeted explanations. This work also includes an initial investigation into the causes of privacy leaks. Finally, we discuss unresolved issues and prospective research directions uncovered in our analysis. This survey aims to be a valuable resource for the research community and offers clear insights for those new to this domain. To support ongoing research, we have established an online resource repository, which will be continuously updated with new and relevant findings.

关键词： privacy-preserving explainable AI privacy attacks privacy defences PrivEx PPXAI

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Efficient and lightweight CNN model for COVID-19 diagnosis from CT and X-ray images using customized pruning and quantization techniques

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Neural Computing and Applications 2025年 1-20页

作者： Sriwiboon, Nattavut Department of Computer Science and Information Technology Faculty of Science and Health Technology Kalasin University Kalasin Thailand

The coronavirus disease 2019 (COVID-19) has posed significant challenges globally, with image classification becoming a critical tool for detecting COVID-19 from chest X-ray and CT images. Convolutional neural network (CNN) architectures are widely employed for this purpose, yet many previous models have struggled to balance high accuracy with low storage requirements, limiting their applicability in resource-constrained environments. This paper introduces an efficient and lightweight CNN model optimized for COVID-19 diagnosis through pruning and quantization techniques, addressing these limitations. Our approach significantly reduces storage size and parameter count while maintaining high accuracy. We customized several CNNs, particularly EfficientNetV2 and MobileNetV2, to achieve these goals. We specifically selected EfficientNetV2 and MobileNetV2 due to their superior balance of accuracy and efficiency, making them well-suited for lightweight medical applications. EfficientNetV2’s storage size has been reduced from 568 to 142 KB through customized pruning, achieving 98.9% accuracy for 2-class and 98.28% for 3-class X-ray classification, as well as 98.3% and 98.05% for 2-class and 3-class CT classification, respectively. MobileNetV2, after hybrid pruning and quantization, achieved a remarkably low storage size of 70 KB, making it highly effective for deployment in environments with limited resources. Despite a slight accuracy decrease of 0.9 to 1.58%, both models have maintained robust performance suitable for COVID-19 diagnosis. The results demonstrate that our pruning and quantization techniques are effective in developing lightweight, high-performance CNN models, making them suitable for broader applications in medical imaging where both efficiency and accuracy are critical. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： COVID-19

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Enhanced Distance-based Weighted K-Nearest Neighbor Algorithm for Data Classification

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KSII Transactions on Internet and information systems 2025年第4期19卷 1097-1121页

作者： Amer, Ali A. Ravana, Sri Devi Habeeb, Riyaz Ahamed Ariyaluran Department of Information Systems Faculty of Computer Science & Information Technology Universiti Malaya Malaysia

The k-Nearest Neighbors (kNN) algorithm is one of the most widely used techniques for data classification. However, the imbalanced class is a key problem for its declining performance. Therefore, the kNN algorithm is kept updated to mitigate this problem’s effects. Following the pattern of the literature, our paper proposes a novel weighted kNN that aims to significantly reduce the negative consequences of this problem. An enhanced distance-based weighted KNN, EDWkNN, is developed to improve the overall KNN performance. For the test sample, the number of neighbors (k) is initially determined. Next, normalized weights are computed for these neighbors and assigned a value between 0 and 1. The weights of the nearest and farthest neighbors are 1 and 0, respectively. The weight values of the remaining neighbors range from 0 to 1, with the closest one having a heavier weight and the farthest neighbor having a lower weight value. In extreme circumstances, a few neighbors have equal distance from the test sample, leading to assigning uniform weights to each of these neighbors. Further, the EDWkNN considers both the magnitude of the distance and the proximity of neighbors. Comparing EDWkNN to its state-of-the-art rivals, its simplistic architecture and competitive performance demonstrate its uniqueness. In four experimental phases, a thorough assessment study is conducted utilizing four evaluation metrics (accuracy, precision, recall, and MAE) over forty-four datasets. The findings show that, on average and for individual k values, EDWkNN is substantially promising. Copyright © 2025 KSII.

关键词： Nearest neighbor search

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Enhanced model for abstractive Arabic text summarization using natural language generation and named entity recognition

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Neural Computing and Applications 2025年第10期37卷 7279-7301页

作者： Essa, Nada El-Gayar, M.M. El-Daydamony, Eman M. Department of Information Technology Faculty of Computer and Information Science Mansoura University Mansoura35516 Egypt

With the rise of Arabic digital content, effective summarization methods are essential. Current Arabic text summarization systems face challenges such as language complexity and vocabulary limitations. We introduce an innovative framework using Arabic Named Entity Recognition to enhance abstractive summarization, crucial for NLP applications like question answering and knowledge graph construction. Our model, based on natural language generation techniques, adapts to diverse datasets. It identifies key information, synthesizes it into coherent summaries, and ensures grammatical accuracy through deep learning. Evaluated on the EASC dataset, our model achieved a 74% ROUGE1 score and a 97.6% accuracy in semantic coherence, with high readability and relevance scores. This sets a new standard for Arabic text summarization, greatly improving NLP information processing. © The Author(s) 2025.

关键词： Semantics

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An Adaptive Vehicle Location Prediction Using Machine Learning: A Case Study of Campus Shuttle Bus

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IEEE Access 2025年 13卷 78290-78302页

作者： Nandy, Tarak Kolandaisamy, Raenu Md Noor, Rafidah Bhattacharyya, Sananda Kuala Lumpur56000 Malaysia Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer Systems and Technology Kuala Lumpur50603 Malaysia Faculty of Computer Science and Information Technology Kuala Lumpur50603 Malaysia Maldives Business School Department of Information Technology Malé20175 Maldives

Vehicle location prediction and the use of vehicle location tracking are increasingly important topics of discussion among connected vehicle researchers. Location tracking for mobile users is essential due to the correlated services and to improve the quality of service;however, it is challenging. On the other hand, the stateless predictive model is unsuitable due to lower accuracy and mismatched pattern analysis. This paper proposed a vehicle location prediction model using machine learning based on a case study of a campus shuttle bus scenario. Firstly, this paper comprehensively analyzes the recent research on vehicle location prediction models and provides a complete taxonomy. The proposed model uses the Support Vector Regression (SVR) based vehicle location predictive model in the implementation phase. In addition, a significant time-series predictive model, ARIMA, is configured and tested. Moreover, the seasonal ARIMA model is experimented with to predict the location of a mobile vehicle. The whole experiment is performed on a real dataset based on the university shuttle bus service. The results of the experiments, analysis, and discussions on the proposed model show the accuracy and effectiveness of its use on others. As a result, seasonal ARIMA outperformed ARIMA and SVR infractions. © 2013 IEEE.

关键词： Prediction models

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Single-Valued Neutrosophic Graph with Heptapartitioend Structure

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Neutrosophic Sets and systems 2025年 80卷 728-748页

作者： Fathi, Hanaa Myvizhi, M. Abdelhafeez, Ahmed Abdellah, Mohamed Refaat Eassa, Mohamed Sawah, Mohamed S. Elbehiery, Hussam College of Information Technology Amman Arab University Amman Jordan KPR Institute of Engineering and Technology Tamilnadu Coimbatore India Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan The Department of Computer Science College of Information Technology Misr University for Science and Technology Cairo Egypt Department of Computer Science Faculty of Information Technology Ajloun National University P.O.43 Ajloun26810 Jordan

This study applies single-valued neutrosophic sets, which extend the frameworks of fuzzy and intuitionistic fuzzy sets, to graph theory. We introduce a new category of graphs called Single-Valued Heptapartitioned Neutrosophic Graphs (SVHNG) and investigate their characteristics through comprehensive proofs and illustrative examples. © 2025, University of New Mexico. All rights reserved.

关键词： Heptapartitioend Neutrosophic Set Neutrosophic Graph Neutrosophic Sets SVHN-Graph

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Advanced Machine Learning Approaches for Breast Cancer Detection with Neutrosophic Sets

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Neutrosophic Sets and systems 2025年 81卷 273-284页

作者： Elbehiery, Hussam Fathi, Hanaa Eassa, Mohamed Abdelhafeez, Ahmed Abdellah, Mohamed Refaat Mahmoud, Hadeer Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt College of Information Technology Amman Arab University Amman Jordan Applied Science Research Center Applied Science Private University Amman Jordan The Department of Computer Science College of Information Technology Misr University for Science and Technology Cairo Egypt Department of Artificial Intelligence Faculty of Computers and Artificial Intelligence Modern University for Technology & Information Cairo Egypt

Breast Cancer (BC) remains a significant health challenge for women and is one of the leading causes of mortality worldwide. Accurate diagnosis is critical for successful therapy and increased survival rates. Recent advances in medical imaging and computational technologies have enabled more precise methods of detecting and evaluating breast cancer. Accurate analysis and diagnosis utilizing medical imaging have developed as essential research topics, providing important help in clinical decision-making for various illnesses, including breast cancer. Machine learning (ML) can accurately predict breast cancer. But the breast cancer data has vague and uncertainty information. So, the neutrosophic sets (NSs) are used in this study to deal with uncertainty data. We convert the original dataset into neutrosophic data with three components such as truth, indeterminacy, and falsity values. Then we applied four ML models with N-data such as logistic regression, gradient boosting (GB), k-nearest neighbor (KNN), and support vector machines (SVM), to improve diagnostic accuracy. Then we compared the ML models with and without using N-data. The results show the logistic regression has higher accuracy with 98.6% with the N-data and 95.80% without N-data. So, the NSs can improve the accuracy of ML models. © 2025, University of New Mexico. All rights reserved.

关键词： Lung cancer

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Computationally Efficient Single Layer Transformer Convolutional Encoder for Accurate Price Prediction of Agriculture Commodities

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IEEE Access 2025年 13卷 82144-82159页

作者： Bundak, Caceja Elyca Anak Abd Rahman, Mohd Amiruddin Haniff, Nurin Syazwina Mohd Afrizal, Nur Syaiful Yusof, Khairul Adib Karim, Muhammad Khalis Abdul Mamat, Md. Shuhazlly Rahmat, Romi Fadillah Faculty of Science Department of Physics Serdang43400 Malaysia Universitas Sumatera Utara Faculty of Computer Science and Information Technology Department of Information Technology Medan20155 Indonesia

Predicting the accurate future price of the agricultural crops is important to avoid overproduction or shortages in the food supply chain. To obtain accurate predictions, the process usually involves large and complex datasets, which would add to computational costs for developing a model with good performance. Therefore, this study introduces the single-layer Transformer Convolutional Encoder algorithm (STCE), an improved version of the traditional transformer encoder. STCE is computationally efficient and does not compromise the accuracy of the prediction. In STCE, the fully connected Convolutional Neural Network (CNN) layer is used in the transformer to get the first temporal features and record long-range dependencies with Multi-Head Attention. To minimize complexity while maintaining performance, a single dense layer is used for the output instead of the Multi-Layer Perceptron (MLP) and omit positional encoding, which leverages the natural sequence order of the time series data. Additionally, since time-series price data normally comes with missing values, this study introduce a sequence nearest neighbor imputation algorithm for anchoring that data to complement the STCE method. This study focuses on various vegetable prices, such as tomatoes, long beans, and cucumbers, with empirical validation across various prediction prices, specifically 30-day, 60-day, and 90-day predictions. Predictions made with Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) show that the STCE algorithm is better than other deep learning algorithms, even the traditional transformer encoder. STCE algorithm not only has better performance, but it also reduces the computational time in the training with 12% fewer seconds compared to the transformer encoder and 22% fewer seconds for LSTM. This study not only provides valuable insights for farmers and planners in the agriculture market but also highlights the robust potential of transforme

关键词： Crops

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Malicious iOS Apps Detection Through Multi-Criteria Decision-Making Approach

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第1期49卷 207-220页

作者： Bhatt, Arpita Jadhav Sardana, Neetu Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Indonesia

In today’s era, smartphones are used in daily lives because they are ubiquitous and can be customized by installing third-party apps. As a result, the menaces because of these apps, which are potentially risky for user’s privacy, have increased. information on smartphones is perhaps, more personal than compared to data stored on desktops or computers, making it an easy target for intruders. After Android, the most prevalently used mobile operating system is Apple’s iOS. Both Android and iOS follow permission-based access control to protect user’s privacy. However, the users are unaware whether the app is breaching the user’s privacy. To combat this problem, in the paper we propose a hybrid approach to detect malicious iOS apps based on its permissions. In the first phase, weights have been assigned to app permissions using multi-criteria decision-making (MCDM) approach namely Analytic Hierarchy Process (AHP), and in the second phase machine learning& ensemble learning techniques have been employed to train the classifiers for detecting malicious apps. To test the efficacy of the proposed method dataset comprising 1150 apps from 12 app categories has been used. The results demonstrate the proposed approach improves the efficacy of detecting malicious iOS apps for majority of categories. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Differential privacy

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Biometric-Based Secure Encryption Key Generation Using Convolutional Neural Networks and Particle Swarm Optimization

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第16期49卷 213-234页

作者： Almola, Sahera A.S. Khudeyer, Raidah S. Younis, Hameed Abdulkareem Department of Computer Information Systems College of Computer Science and Information Technology University of Basrah Basrah Iraq

With the rapid expansion of computer networks and information technology, ensuring secure data transmission is increasingly vital—especially for image data, which often contains sensitive information. This research presents a biometric-based encryption system that uses fingerprint recognition and deep learning to generate strong, random encryption keys. Two convolutional neural networks (CNNs) are employed: one to verify identity based on a user’s ID and another to extract fingerprint features for key generation. These keys are optimized using Particle Swarm Optimization (PSO), enhancing their randomness and resistance to brute-force attacks. The system generates keys in real-time, eliminating the need for storage and minimizing the risk of theft or leakage. To further improve security, encryption keys are automatically updated after every ten messages, with different keys generated from multiple fingerprints of the same individual. Testing with the SOCOFing dataset (6,000 original and 49,270 synthetic images) achieved 99.75% identity verification and 99.83% classification accuracy. Performance metrics—entropy of 7.89, correlation factor of 0.00628, and zero repetition—demonstrate high robustness. This approach offers a secure, adaptive, and personalized encryption method ideal for sensitive domains like finance and healthcare. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Convolutional neural networks

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