隐私计算与数据要素 · 全部实体（第 11 / 100 页）

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• Privacy-Preserving Fingerprint Authentication for IoT Using Homomorphic Encryption
  crossref Khang Ngo Hoang Nhat, Tuan Ho Anh, Tung Dam Minh 2026-08-11T19:17:04Z 置信度 0.70
• Efficient Privacy-Preserving Storage Framework for Accounting Feature Data Integrating Revocable Template Mapping and Homomorphic Encryption Computation
  crossref Wang Jinsong 2026-07-27T19:15:31Z 置信度 0.70
• Efficient Arithmetic-and-Comparison Homomorphic Encryption with Space Switching
  crossref Erwin Eko Wahyudi, Yan Solihin, Qian Lou 2026-07-01T19:34:20Z 置信度 0.70
• A Privacy-Preserving Quadratic Optimisation with Additive Homomorphic Encryption in Cyber-Physical Systems
  In this paper, we propose a secure protocol to compute the quadratic optimisation problem under a three-party outsourcing architecture in the scenario of cyber-physical systems. To enable real-world implementation, we propose an encoding framework that uses a …
  crossref Ying He, Yang Pu, Rui Ye, Zhenyong Zhang 2026-05-01T10:15:27Z 置信度 0.70
• FPGA Implementation of Efficient NTT Hardware Accelerator for Fully Homomorphic Encryption
  crossref Daniel Tselogorodtsev, Liam Sittig, Hannah Robinson, Jiafeng Xie 2026-06-09T19:50:08Z 置信度 0.70
• Ensuring Fairness of Parties via Homomorphic Encryption over Multiparty Protocol in Two Different Schemes
  crossref Akshit Aggarwal 2026-04-26T17:25:14Z 置信度 0.70
• FPGA-Based Field Arithmetic Logic Unit for Homomorphic Encryption
  crossref Zeinab A. Hassaan, Nermeen H. Abdelzaher, Ghada Bouattour, Lobna A. Said 2026-07-31T18:16:00Z 置信度 0.70
• Securing Federated Learning in Health IoT with Edge-Assisted Homomorphic Encryption
  crossref Rahul Kavati, Sriven Srilakshmi Pulkaram, Huzaif Khan, Ali Jalooli 2026-02-25T20:53:57Z 置信度 0.70
• Privacy at your Fingertips: Enabling Rapid Client-Side Operations in Fully Homomorphic Encryption
  crossref Aikata Aikata, Florian Krieger, Sujoy Sinha Roy 2026-07-30T19:04:13Z 置信度 0.70
• A secure two-party polygon intersection area computation protocol based on quantum homomorphic encryption
  Abstract Secure Multi-party Geometric Computation(SMGC) is an important field in Secure Multi-party Computation(SMC). It aims to enable multiple mutually distrustful parties to collaboratively perform geometric computation tasks while protecting their respecti…
  crossref Bai Liu, Xin-Guo Wang 2026-05-26T08:47:57Z 置信度 0.70
• A Privacy-Preserving Aggregation Paradigm of Distributed Energy Resources using Homomorphic Encryption
  crossref Ziyi Zhao, Yichen Zhang, Wei-Jen Lee 2026-05-15T19:51:24Z 置信度 0.70
• Selective Homomorphic Encryption With LLE Enhances Privacy and Scalability in Doorbell Face Recognition
  crossref Raniyah Wazirali, Fatma Foad Ashrif, Rami Ahmad 2026-02-06T20:51:07Z 置信度 0.70
• Privacy-Preserving Deep Learning for Multi-Tenant CRM Workflows: Integrating Homomorphic Encryption across Cloud Databases
  crossref Abhaar Gupta, Tarun Vishwanath Chincholi, Supreet Nagi, Balaje Prasath Manoharan 2026-05-12T19:46:43Z 置信度 0.70
• Privacy-Preserving Transformer Inference with Optimized Homomorphic Encryption and Secure Collaborative Computing
  crossref Tao Bai, Yang Tang, Kuan Shao, Zhenyong Zhang 等 2026-04-07T08:46:53Z 置信度 0.70
• A bibliometric analysis of Homomorphic Encryption for privacy-preserving biometrics
  crossref Shreyansh Sharma, Anurag Mudgil, Richa Dubey, Anil Saini 等 2026-01-17T14:22:53Z 置信度 0.70
• Secure machine learning on encrypted data using homomorphic encryption techniques
  Privacy preservation has become a critical challenge in modern machine learning applications, especially in sensitive domains such as healthcare, finance, and cloud-based services, where confidential data must be protected. This paper aims to develop a secure …
  crossref Sushma M P, Kiran Puttegowda, Praveenkumara J 2026-07-15T10:23:24Z 置信度 0.70
• SECURE FEDERATED INTRUSION DETECTION USING HOMOMORPHIC ENCRYPTION: A COMPARATIVE STUDY WITH ENSEMBLE LEARNING
  The rapid growth of the Internet of Things (IoT) has also resulted in the increase of the demand in the intrusion detection systems, which can detect suspicious activity and keep the information confidential. The traditional centralized machine learning system…
  crossref Sa daf, Aasim Zafar, Mohammad Luqman 2026-08-12T03:43:10Z 置信度 0.70
• ABS: An Adaptive Bootstrapping Scheduler for Efficient and Accurate Logistic Regression Training over Homomorphic Encryption
  Homomorphic Encryption (HE) presents a com pelling paradigm for privacy-preserving machine learning; how ever, its practical adoption is hindered by substantial computa tional overhead. For iterative algorithms such as Logistic Regres sion (LR), the main perfo…
  crossref Kaishuo Wang 2026-02-10T04:26:03Z 置信度 0.70
• ReFHE-NTT: Resource-Driven NTT FPGA Architecture for Fully Homomorphic Encryption
  crossref Valentino Guerrini, Giuseppe Sorrentino, Alessandro Barenghi, Davide Conficconi 2026-06-10T19:59:45Z 置信度 0.70
• Multi-Key Homomorphic Encryption with Threshold Re-Encryption via (Replicated) Additive Secret Sharing
  crossref Akira NAKASHIMA, Yukimasa SUGIZAKI, Hikaru TSUCHIDA, Takuya HAYASHI 等 2025-09-25T22:09:41Z 置信度 0.70
• Meeting SLO with Privacy: Partial Homomorphic Encryption Inference and Anomaly Gating for Predictive Autoscaling in Cloud Environments
  crossref Alan Chuang, Melody Moh, Teng-Sheng Moh 2026-03-09T19:55:54Z 置信度 0.70
• Experimental Validation of AUX Scheme for Quantum Homomorphic Encryption on IBM Quantum Platforms
  crossref Gia Phat Dang, Weisheng Si, Belal Alsinglawi, Jim Basilakis 2026-05-07T19:51:14Z 置信度 0.70
• A Privacy-Preserving TF-IDF Neural Network Framework with Fully Homomorphic Encryption for Medical PII Text Analytics
  crossref Uchenna Ndolo, Hoda El-Sayed 2026-08-06T19:09:30Z 置信度 0.70
• Tri-layered Hybrid Privacy Architectures for Deep Neural Networks: Synergizing Split Learning, Differential Privacy, and CKKS Homomorphic Encryption
  crossref Wisdom C. Amadi, Daniel Ekpah 2026-08-20T10:38:02Z 置信度 0.70
• Homomorphic Encryption (HE), Artificial Intelligence (AI): An Analysis, Significance and Applications with Coding in Python
  crossref Awadhesh Kumar Mishra, Pinki Tomar, Roushan kumar sharma 2026-06-30T16:41:54Z 置信度 0.70
• Structural Testing with Homomorphic Encryption
  crossref Andrei Aleksandrov 2026-04-01T14:57:16Z 置信度 0.70
• Analysis of various homomorphic encryption algorithms based on primitive functions and applications
  crossref J. Josepha Menandas, Mary Subaja Christo 2025-06-28T04:19:57Z 置信度 0.70
• Orion: A Fully Homomorphic Encryption Framework for Deep Learning
  Fully Homomorphic Encryption (FHE) has the potential to substantially improve privacy and security by enabling computation directly on encrypted data. This is especially true in deep learning, as many popular user services today are powered by neural networks …
  crossref Austin Ebel, Karthik Garimella, Brandon Reagen 2026-07-22T11:06:26Z 置信度 0.70
• A Secure Comparison Protocol Based on the SM2 Homomorphic Encryption Algorithm
  crossref Xiao Deng, Shutong Li, Yuxin Li, Baodong Qin 2026-05-02T02:34:57Z 置信度 0.70
• Optimizing Cloud Data Security using Homomorphic Encryption: A Hybrid Paillier and ElGamal Approach
  crossref Saranmani M, Rajasethupathi G, Manikandan S, S.Nithya 等 2026-06-23T19:43:46Z 置信度 0.70
• A Privacy-Preserving Federated Learning Framework with Fully Homomorphic Encryption for Reproductive Health Analytics
  With the increasing use of cloud analytics technology, machine learning is now being used to help with fertility tracking and predict risks of pregnancy. However, reproductive health data is considered highly sensitive data, and with traditional analytics trai…
  crossref Abass Hassan, Sheikh Umar Mushtaq, Hussein Edrees, Amier Alquatesh 2026-07-18T11:10:17Z 置信度 0.70
• Encrypted intelligence: A comparative analysis of homomorphic encryption frameworks for privacy-preserving AI
  crossref Aadit Shah, Surindernath Sivakumar, Prabakaran N 2025-08-29T23:33:08Z 置信度 0.70
• Presto: Hardware Acceleration of Ciphers for Hybrid Homomorphic Encryption
  crossref Yeonsoo Jeon, Lewis Liu, Mattan Erez, Michael Orshansky 2026-08-10T19:23:31Z 置信度 0.70
• Design and performance optimization of lightweight homomorphic encryption scheme for optical MPC
  crossref Liehua Peng 2026-01-31T16:10:53Z 置信度 0.70
• Cryptographically Computable Self-Learning Activation Functions for Privacy-Preserving Logistic Regression with Homomorphic Encryption
  crossref Bernardo Pulido-Gaytan, Horacio González-Vélez, Andrei Tchernykh 2026-08-19T19:12:52Z 置信度 0.70
• Improvements of Homomorphic Encryption Algorithms and Their Effects in Privacy Protection in the Internet of Things
  ABSTRACT At present, traditional homomorphic encryption (HE) algorithms face the problems of high computational consumption and weak antiattack ability in complex environments. In order to handle these shortcomings and improve the HE algorithm, the ring learni…
  crossref Xuewei Li, Shulai Chu 2026-05-28T07:29:38Z 置信度 0.70
• FedOut: Federated learning with outsourced homomorphic encryption for resource-constrained IoT devices
  crossref Yi Yuan, Hong Rao, Shuanggen Liu, Rixuan Qiu 等 2026-08-21T10:03:53Z 置信度 0.70
• A High Performance Hardware Accelerator for Fully Homomorphic Encryption and Application to Neural Networks
  crossref Chuanxin Zhang, Xiaojie Zhu, Chi Chen 2026-04-21T21:23:31Z 置信度 0.70
• Privacy-Preserving Digital Evidence Chain for Accounting Vouchers Based on Smart Contracts and Paillier Homomorphic Encryption
  Electronic accounting voucher deposits and cybersecurity audits face a contradiction between resistance to evidence tampering and data privacy protection. Using blockchain technology as a basis, the authors propose a solution framework that addresses both conc…
  crossref Hui Sui, Ying Sui 2026-08-19T19:48:16Z 置信度 0.70
• A verifiable privacy-preserving federated learning scheme based on homomorphic proxy re-encryption
  crossref Zeyu Song, Zhenjie Huang, Yiping Cai 2025-11-13T10:20:23Z 置信度 0.70
• The HHE Land: Exploring the Landscape of Hybrid Homomorphic Encryption
  Hybrid Homomorphic Encryption (HHE) addresses key challenges in Homomorphic Encryption (HE), such as communication, computation, and storage overheads, by combining symmetric cryptography with HE schemes. Despite progress, enhancing HHE’s usability, performanc…
  crossref Hossein Abdinasibfar, Camille Nuoskala, Antonis Michalas 2026-04-08T11:45:37Z 置信度 0.70
• Securing Healthcare Data with Homomorphic Encryption
  crossref M. Jeyaselvi, Vamsi Yanamadala, Sai P. Kethan 2026-02-25T15:21:08Z 置信度 0.70
• Fully Homomorphic Encryption for Secure Cloud Computation
  Fully Homomorphic Encryption (FHE) is an advanced cryptographic technique that enables computation on encrypted data without requiring decryption. This capability eliminates the need to expose sensitive data during processing, making FHE particularly suitable …
  crossref Sreekutty Sabarivasan, Ashish L. 2026-03-07T07:02:38Z 置信度 0.70
• Efficient privacy-preserving sparse matrix-vector multiplication using homomorphic encryption
  crossref Yang Gao, Gang Quan, Wujie Wen, Scott Piersall 等 2026-01-31T15:45:41Z 置信度 0.70
• A Multiphase Zero-Trust Authentication Framework Using Replicated and Homomorphic Encryption
  crossref Modisaotsile Marope, Venumadhav Kuthadi, Rajalakshmi Selvaraj, Thabo Semong 等 2026-02-27T11:32:34Z 置信度 0.70
• Time-Area Efficient RNS Base-conversion Architecture for HPS-BFV Homomorphic Encryption using Generalized Solinas Primes
  crossref Rella Mareta, Ardianto Satriawan, Hanho Lee 2026-06-18T20:06:41Z 置信度 0.70
• Efficient Federated Learning with Low-Rank Updates under Homomorphic Encryption
  crossref Mohamed Aboelenien Ahmed, Mohamed Alsharkawy, Hassan Nassar, Heba Khdr 等 2026-06-04T19:53:10Z 置信度 0.70
• Adaptive Multi-Tier Fully Homomorphic Encryption with Differential Privacy for Privacy-Preserving Medical Data Classification
  crossref Charvi Palem, Servani Veeranki, Vihaan Reddy Thatiparthi, Kamalakanta Sethi 2026-05-08T19:37:10Z 置信度 0.70
• Implementing Post-Quantum Homomorphic Encryption in the Eclipse Qrisp High-Level Programming Language for Quantum Computing
  crossref Aurelia Kusumastuti, Nikolay Tcholtchev, Philipp Lämmel, Sebastian Bock 等 2026-08-27T19:08:30Z 置信度 0.70
• Energy Consumption of TLS, Searchable Encryption and Fully Homomorphic Encryption
  crossref Marc Damie, Mihai Pop, Merijn Posthuma 2026-05-11T00:20:32Z 置信度 0.70
• Privacy-Preserving State Estimation: An Encrypted Extended Kalman Filter Using CKKS Homomorphic Encryption
  Fully Homomorphic Encryption (FHE) enables mathematical operations directly on encrypted data without decryption. Any operation that a polynomial can approximate can, in principle, be executed under an FHE scheme. To protect cyber-physical systems from eavesdr…
  crossref Michael Sotula Masangu, Moanda Ndeko Mosengo C. M., Witesyavwirwa Vianney Kambale, Kyandoghere Kyamakya 2026-04-21T09:49:37Z 置信度 0.70
• A Performance Study of Fully Homomorphic Encryption for Secure Healthcare Applications
  crossref Ramachandra H. N., S. Mahamayi, Manasa, V. K. Nandana 等 2026-08-13T19:16:12Z 置信度 0.70
• Research on a Universal Data Privacy Collaborative Protection Technology that Integrates Homomorphic Encryption and Secure Multi-party Computation
  In big data interoperability scenarios, single privacy protection technologies struggle to balance the demands of efficient data processing with stringent privacy security requirements. Homomorphic encryption and secure multi-party computation, two core techno…
  crossref Wenliang Tian 2026-07-22T03:08:52Z 置信度 0.70
• Secure and Efficient Federated Learning Using Sketches and Fully Homomorphic Encryption
  Federated Learning (FL) represents a distributed training paradigm designed to enable collaborative model training without sharing raw data. However, sophisticated attackers can infer or reconstruct sensitive client data from shared model updates, thereby weak…
  crossref Eduardo M. M. Sarmento, João Pedro Camargo Batista, Johann Jakob Schmitz Bastos, Vinicius Fernandes Soares Mota 等 2026-04-04T16:38:32Z 置信度 0.70
• Privacy-preserving worker selection in spatial crowdsourcing based on quantum homomorphic encryption
  In spatial crowdsourcing tasks, the risk of privacy leakage regarding task and workers’ locations has always been a critical issue. Although many scholars have proposed solutions based on classical computing, these methods cannot withstand potential future qua…
  crossref Bai Liu, Shupin Qiu, Mingwu Zhang, Xinguo Wang 等 2026-04-07T13:17:13Z 置信度 0.70
• Privacy-Preserving Fingerprint Authentication Using SaDeXNet and Fully Homomorphic Encryption
  crossref Pavani Chitrapu, Hemantha Kumar Kalluri, Mahesh Kumar Morampudi, Rusydi Umar 2026-01-07T20:02:33Z 置信度 0.70
• Privacy-preserving homomorphic encryption in cloud storage
  crossref Noor R. Obeid, Noor Fadel, Dalia Abdulrahim Mokheef 2026-02-27T18:00:41Z 置信度 0.70
• CryptoESN: Privacy-Preserving Echo State Network Using Homomorphic Encryption
  crossref Tanuja, Rakesh Kumar 2026-01-02T01:27:18Z 置信度 0.70
• AHE: Adaptive Homomorphic Encryption for Customizable Privacy in Heterogeneous Federated Learning
  crossref Jiaxiang Tang, Xinran Wang, Qi Le, Kangjie Lu 等 2026-06-29T19:38:15Z 置信度 0.70
• Secure numerical simulations using fully homomorphic encryption
  crossref Arseniy Kholod, Yuriy Polyakov, Michael Schlottke-Lakemper 2025-09-19T16:30:49Z 置信度 0.70
• Privacy-Preserving Post Deployment Model Calibration with Fully Homomorphic Encryption
  crossref Shadman Mahmood Khan Pathan, Qianlong Wang, Jonathan Takeshita, Sakan Binte Imran 等 2026-03-09T19:55:54Z 置信度 0.70
• Enhanced privacy-preserving neural networks with fully homomorphic encryption: Optimized search and training
  crossref Peng Zhang, Xiang Li, Jianwen Liang 2026-06-11T20:45:23Z 置信度 0.70
• Secure Paternity Testing with Homomorphic Encryption
  crossref Nicole Anne Balde, Richard Bryann Chua 2026-04-30T01:48:56Z 置信度 0.70
• FHEIns: Fully Homomorphic Encryption Acceleration for Large Data Applications with In-Storage Processing
  crossref Xuan Wang, Tianqi Zhang, Keming Fan, Augusto Vega 等 2026-06-04T19:53:10Z 置信度 0.70
• HE-DAP: Homomorphic Encryption-based Dynamic Adaptive Parameter Optimization for Statistical Computation
  crossref Yun-Soo Park, Hyunmin Choi, Hyoungshick Kim, Mun-Kyu Lee 2026-06-09T14:17:49Z 置信度 0.70
• Framework for Fully Homomorphic Encryption Libraries Comparative Analysis: Case Study for Microsoft SEAL, OpenFHE, and HElib
  crossref Antal Marcel, Ioan Andrei Șpac, Bogdan Nicusor Bindea 2026-07-01T19:34:29Z 置信度 0.70
• PCPSI: Efficient Unbalanced Private Set Intersection Using Homomorphic Encryption With Plaintext–Ciphertext Multiplication
  crossref Dongju Lee, Sungyeon Lee, Youyeon Joo, Kevin Nam 等 2026-06-08T19:52:56Z 置信度 0.70
• Development and Performance Evaluation of a Modified Homomorphic Encryption Scheme for ESP32-Based Resource-Constrained IoT Devices
  crossref Zhansaya Myrzakul, Zhanerke Temirbekova, Gulzhan Myrzakul 2026-07-14T19:38:14Z 置信度 0.70
• HARPOCRATES Newsletter – Looking Back, Moving Forward (September 2025)
  This newsletter from the Horizon Europe project HARPOCRATES (2022–2025) presents recent progress and activities on advancing privacy-preserving technologies for secure data sharing and analysis. Coordinated by Tampere University, the consortium of 13 partners …
  datacite Vasic, Jelena 2025 置信度 0.66
• HARPOCRATES Newsletter – Looking Back, Moving Forward (September 2025)
  This newsletter from the Horizon Europe project HARPOCRATES (2022–2025) presents recent progress and activities on advancing privacy-preserving technologies for secure data sharing and analysis. Coordinated by Tampere University, the consortium of 13 partners …
  datacite Vasic, Jelena 2025 置信度 0.66
• Effet Flynn Inverse : Analyse des Causes, Solutions et Scénarios Prospectifs
  Abstract ENThis document, produced with the assistance of ChatGPT o3 and GPT-5 Thinking, is released under the Apache 2.0 license. It is a voluntary defensive publication (prior art) and therefore enters the prior art upon release under the applicable patent s…
  datacite Pillet, Xavier 2025 置信度 0.66 adaptive tutoringmastery learningreinforcement learningEEG headsetneurofeedback
• Effet Flynn Inverse : Analyse des Causes, Solutions et Scénarios Prospectifs
  Abstract ENThis document, produced with the assistance of ChatGPT o3 and GPT-5 Thinking, is released under the Apache 2.0 license. It is a voluntary defensive publication (prior art) and therefore enters the prior art upon release under the applicable patent s…
  datacite Pillet, Xavier 2025 置信度 0.66 adaptive tutoringmastery learningreinforcement learningEEG headsetneurofeedback
• Effet Flynn Inverse : Analyse des Causes, Solutions et Scénarios Prospectifs
  Abstract ENThis document, produced with the assistance of ChatGPT o3 and GPT-5 Thinking, is released under the Apache 2.0 license. It is a voluntary defensive publication (prior art) and therefore enters the prior art upon release under the applicable patent s…
  datacite Pillet, Xavier 2025 置信度 0.66 adaptive tutoringmastery learningreinforcement learningEEG headsetneurofeedback
• Federated Learning for Privacy-Preserving Healthcare AI Models
  Federated learning (FL) has emerged as a transformative paradigm for building collaborative healthcare AI models while safeguarding patient privacy and complying with regulations such as HIPAA and GDPR. Unlike centralized training, FL enables multiple hospital…
  datacite SHYAM SUNDER SAINI 2025 置信度 0.66 Federated learningPrivacy-preservingHealthcareSecure aggregationDifferential privacy
• Federated Learning for Privacy-Preserving Healthcare AI Models
  Federated learning (FL) has emerged as a transformative paradigm for building collaborative healthcare AI models while safeguarding patient privacy and complying with regulations such as HIPAA and GDPR. Unlike centralized training, FL enables multiple hospital…
  datacite SHYAM SUNDER SAINI 2025 置信度 0.66 Federated learningPrivacy-preservingHealthcareSecure aggregationDifferential privacy
• Privacy-Preserving Federated Learning via Homomorphic Adversarial Networks
  Privacy-preserving federated learning (PPFL) aims to train a global model for multiple clients while maintaining their data privacy. However, current PPFL protocols exhibit one or more of the following insufficiencies: considerable degradation in accuracy, the…
  datacite Dong, Wenhan, Lin, Chao, He, Xinlei, Xu, Shengmin 等 2024 置信度 0.66 Cryptography and Security (cs.CR)Artificial Intelligence (cs.AI)Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• A review article on "Federated Learning on Cloud Platforms: Privacy-Preserving Machine Learning Across Distributed Systems"
  Federated Learning (FL) is a novel decentralized machine learning paradigm that enables collaborative model training across multiple clients without sharing raw data, thereby preserving privacy. This review synthesizes nineteen pivotal studies from 2017 to 202…
  datacite PENUMUCHU 2025 置信度 0.66 Federated Learning; Privacy; Distributed AI; Healthcare; Ethics; Personalization; Indian Data Protection; Communication Efficiency; Data Partitioning; Interpretability
• A review article on "Federated Learning on Cloud Platforms: Privacy-Preserving Machine Learning Across Distributed Systems"
  Federated Learning (FL) is a novel decentralized machine learning paradigm that enables collaborative model training across multiple clients without sharing raw data, thereby preserving privacy. This review synthesizes nineteen pivotal studies from 2017 to 202…
  datacite PENUMUCHU 2025 置信度 0.66 Federated Learning; Privacy; Distributed AI; Healthcare; Ethics; Personalization; Indian Data Protection; Communication Efficiency; Data Partitioning; Interpretability
• Federated Learning: An approach with Hybrid Homomorphic Encryption
  Federated Learning (FL) is a distributed machine learning approach that promises privacy by keeping the data on the device. However, gradient reconstruction and membership-inference attacks show that model updates still leak information. Fully Homomorphic Encr…
  datacite Correia, Pedro, Silva, Ivan, Amorim, Ivone, Maia, Eva 等 2025 置信度 0.66 Cryptography and Security (cs.CR)FOS: Computer and information sciencesFOS: Computer and information sciencesE.3; C.2.0; C.2.4
• Efficient Privacy-Preserving Recommendation on Sparse Data using Fully Homomorphic Encryption
  In today's data-driven world, recommendation systems personalize user experiences across industries but rely on sensitive data, raising privacy concerns. Fully homomorphic encryption (FHE) can secure these systems, but a significant challenge in applying FHE t…
  datacite Chowdhury, Moontaha Nishat, Bauer, André, Zhou, Minxuan 2025 置信度 0.66 Cryptography and Security (cs.CR)Artificial Intelligence (cs.AI)Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• Breaking the Layer Barrier: Remodeling Private Transformer Inference with Hybrid CKKS and MPC
  This paper presents an efficient framework for private Transformer inference that combines Homomorphic Encryption (HE) and Secure Multi-party Computation (MPC) to protect data privacy. Existing methods often leverage HE for linear layers (e.g., matrix multipli…
  datacite Xu, Tianshi, Lu, Wen-jie, Yu, Jiangrui, Yi, Chen 等 2025 置信度 0.66 Cryptography and Security (cs.CR)FOS: Computer and information sciencesFOS: Computer and information sciences
• CryptoFace: End-to-End Encrypted Face Recognition
  Face recognition is central to many authentication, security, and personalized applications. Yet, it suffers from significant privacy risks, particularly arising from unauthorized access to sensitive biometric data. This paper introduces CryptoFace, the first …
  datacite Ao, Wei, Boddeti, Vishnu Naresh 2025 置信度 0.66 Computer Vision and Pattern Recognition (cs.CV)Cryptography and Security (cs.CR)FOS: Computer and information sciencesFOS: Computer and information sciences
• Leuvenshtein: Efficient FHE-based Edit Distance Computation with Single Bootstrap per Cell
  This paper presents a novel approach to calculating the Levenshtein (edit) distance within the framework of Fully Homomorphic Encryption (FHE), specifically targeting third-generation schemes like TFHE. Edit distance computations are essential in applications …
  datacite Legiest, Wouter, D'Anvers, Jan-Pieter, Spasic, Bojan, Tran, Nam-Luc 等 2025 置信度 0.66 Cryptography and Security (cs.CR)FOS: Computer and information sciencesFOS: Computer and information sciencesE.3
• Efficient and Verifiable Privacy-Preserving Convolutional Computation for CNN Inference with Untrusted Clouds
  The widespread adoption of convolutional neural networks (CNNs) in resource-constrained scenarios has driven the development of Machine Learning as a Service (MLaaS) system. However, this approach is susceptible to privacy leakage, as the data sent from the cl…
  datacite Lu, Jinyu, Sun, Xinrong, Tao, Yunting, Ji, Tong 等 2025 置信度 0.66 Cryptography and Security (cs.CR)Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• Trans-XFed: An Explainable Federated Learning for Supply Chain Credit Assessment
  This paper proposes a Trans-XFed architecture that combines federated learning with explainable AI techniques for supply chain credit assessment. The proposed model aims to address several key challenges, including privacy, information silos, class imbalance, …
  datacite Shi, Jie, Siebes, Arno P. J. M., Mehrkanoon, Siamak 2025 置信度 0.66 Machine Learning (cs.LG)Distributed, Parallel, and Cluster Computing (cs.DC)FOS: Computer and information sciencesFOS: Computer and information sciences
• Empirical Analysis of Privacy-Fairness-Accuracy Trade-offs in Federated Learning: A Step Towards Responsible AI
  Federated Learning (FL) enables collaborative model training while preserving data privacy; however, balancing privacy preservation (PP) and fairness poses significant challenges. In this paper, we present the first unified large-scale empirical study of priva…
  datacite Wasif, Dawood, Chen, Dian, Madabushi, Sindhuja, Alluru, Nithin 等 2025 置信度 0.66 Machine Learning (cs.LG)Cryptography and Security (cs.CR)Distributed, Parallel, and Cluster Computing (cs.DC)Emerging Technologies (cs.ET)FOS: Computer and information sciences
• IDFace: Face Template Protection for Efficient and Secure Identification
  As face recognition systems (FRS) become more widely used, user privacy becomes more important. A key privacy issue in FRS is protecting the user's face template, as the characteristics of the user's face image can be recovered from the template. Although rece…
  datacite Kim, Sunpill, Paik, Seunghun, Hwang, Chanwoo, Kim, Dongsoo 等 2025 置信度 0.66 Cryptography and Security (cs.CR)Computer Vision and Pattern Recognition (cs.CV)FOS: Computer and information sciencesFOS: Computer and information sciencesI.5.4; K.6.5; D.4.6; I.4.7
• DESIGN: Encrypted GNN Inference via Server-Side Input Graph Pruning
  Graph Neural Networks (GNNs) have achieved state-of-the-art performance in various graph-based learning tasks. However, enabling privacy-preserving GNNs in encrypted domains, such as under Fully Homomorphic Encryption (FHE), typically incurs substantial comput…
  datacite Zhao, Kaixiang, Attalla, Joseph Yousry, Lou, Qian, Dong, Yushun 2025 置信度 0.66 Cryptography and Security (cs.CR)Artificial Intelligence (cs.AI)Machine Learning (cs.LG)FOS: Computer and information sciences
• QuHE: Optimizing Utility-Cost in Quantum Key Distribution and Homomorphic Encryption Enabled Secure Edge Computing Networks
  Ensuring secure and efficient data processing in mobile edge computing (MEC) systems is a critical challenge. While quantum key distribution (QKD) offers unconditionally secure key exchange and homomorphic encryption (HE) enables privacy-preserving data proces…
  datacite Qian, Liangxin, Li, Yang, Zhao, Jun 2025 置信度 0.66 Social and Information Networks (cs.SI)FOS: Computer and information sciencesFOS: Computer and information sciences
• FIDESlib: A Fully-Fledged Open-Source FHE Library for Efficient CKKS on GPUs
  Word-wise Fully Homomorphic Encryption (FHE) schemes, such as CKKS, are gaining significant traction due to their ability to provide post-quantum-resistant, privacy-preserving approximate computing; an especially desirable feature in Machine-Learning-as-a-Serv…
  datacite Agulló-Domingo, Carlos, Vera-López, Óscar, Guzelhan, Seyda, Daksha, Lohit 等 2025 置信度 0.66 Cryptography and Security (cs.CR)FOS: Computer and information sciencesFOS: Computer and information sciences
• Can One Safety Loop Guard Them All? Agentic Guard Rails for Federated Computing
  We propose Guardian-FC, a novel two-layer framework for privacy preserving federated computing that unifies safety enforcement across diverse privacy preserving mechanisms, including cryptographic back-ends like fully homomorphic encryption (FHE) and multipart…
  datacite Veeraragavan, Narasimha Raghavan, Nygård, Jan Franz 2025 置信度 0.66 Cryptography and Security (cs.CR)Distributed, Parallel, and Cluster Computing (cs.DC)Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• Relative Entropy Regularized Reinforcement Learning for Efficient Encrypted Policy Synthesis
  We propose an efficient encrypted policy synthesis to develop privacy-preserving model-based reinforcement learning. We first demonstrate that the relative-entropy-regularized reinforcement learning framework offers a computationally convenient linear and ``mi…
  datacite Suh, Jihoon, Jang, Yeongjun, Teranishi, Kaoru, Tanaka, Takashi 2025 置信度 0.66 Machine Learning (cs.LG)Systems and Control (eess.SY)FOS: Computer and information sciencesFOS: Computer and information sciencesFOS: Electrical engineering, electronic engineering, information engineering
• Technical Evaluation of a Disruptive Approach in Homomorphic AI
  We present a technical evaluation of a new, disruptive cryptographic approach to data security, known as HbHAI (Hash-based Homomorphic Artificial Intelligence). HbHAI is based on a novel class of key-dependent hash functions that naturally preserve most simila…
  datacite Filiol, Eric 2025 置信度 0.66 Cryptography and Security (cs.CR)Artificial Intelligence (cs.AI)FOS: Computer and information sciencesFOS: Computer and information sciences
• FicGCN: Unveiling the Homomorphic Encryption Efficiency from Irregular Graph Convolutional Networks
  Graph Convolutional Neural Networks (GCNs) have gained widespread popularity in various fields like personal healthcare and financial systems, due to their remarkable performance. Despite the growing demand for cloud-based GCN services, privacy concerns over s…
  datacite Kan, Zhaoxuan, Han, Husheng, Shi, Shangyi, Hua, Tenghui 等 2025 置信度 0.66 Cryptography and Security (cs.CR)FOS: Computer and information sciencesFOS: Computer and information sciences
• ABC-FHE : A Resource-Efficient Accelerator Enabling Bootstrappable Parameters for Client-Side Fully Homomorphic Encryption
  As the demand for privacy-preserving computation continues to grow, fully homomorphic encryption (FHE)-which enables continuous computation on encrypted data-has become a critical solution. However, its adoption is hindered by significant computational overhea…
  datacite Yune, Sungwoong, Lee, Hyojeong, Putra, Adiwena, Cho, Hyunjun 等 2025 置信度 0.66 Hardware Architecture (cs.AR)Cryptography and Security (cs.CR)Emerging Technologies (cs.ET)FOS: Computer and information sciencesFOS: Computer and information sciences
• GeoAI and its Role in Planetary Health: Navigating the Path to Global Sustainability
  I. Introduction: The Dawn of GeoAI in Planetary Health Stewardship The 21st century is characterized by unprecedented environmental challenges that threaten the delicate balance of Earth's systems and, consequently, human civilization. Climate change, accelera…
  datacite ABHIJEET SARKAR 2025 置信度 0.66
• GeoAI and its Role in Planetary Health: Navigating the Path to Global Sustainability
  I. Introduction: The Dawn of GeoAI in Planetary Health Stewardship The 21st century is characterized by unprecedented environmental challenges that threaten the delicate balance of Earth's systems and, consequently, human civilization. Climate change, accelera…
  datacite ABHIJEET SARKAR 2025 置信度 0.66
• ALLMod: Exploring $\underline{\mathbf{A}}$rea-Efficiency of $\underline{\mathbf{L}}$UT-based $\underline{\mathbf{L}}$arge Number $\underline{\mathbf{Mod}}$ular Reduction via Hybrid Workloads
  Modular arithmetic, particularly modular reduction, is widely used in cryptographic applications such as homomorphic encryption (HE) and zero-knowledge proofs (ZKP). High-bit-width operations are crucial for enhancing security; however, they are computationall…
  datacite Liu, Fangxin, Li, Haomin, Wang, Zongwu, Zhang, Bo 等 2025 置信度 0.66 Cryptography and Security (cs.CR)Hardware Architecture (cs.AR)FOS: Computer and information sciencesFOS: Computer and information sciences
• SONNI: Secure Oblivious Neural Network Inference
  In the standard privacy-preserving Machine learning as-a-service (MLaaS) model, the client encrypts data using homomorphic encryption and uploads it to a server for computation. The result is then sent back to the client for decryption. It has become more and …
  datacite Sperling, Luke, Kulkarni, Sandeep S. 2025 置信度 0.66 Cryptography and Security (cs.CR)FOS: Computer and information sciencesFOS: Computer and information sciences
• EFFACT: A Highly Efficient Full-Stack FHE Acceleration Platform
  Fully Homomorphic Encryption (FHE) is a set of powerful cryptographic schemes that allows computation to be performed directly on encrypted data with an unlimited depth. Despite FHE's promising in privacy-preserving computing, yet in most FHE schemes, cipherte…
  datacite Huang, Yi, Gong, Xinsheng, Kong, Xiangyu, Chen, Dibei 等 2025 置信度 0.66 Cryptography and Security (cs.CR)Hardware Architecture (cs.AR)FOS: Computer and information sciencesFOS: Computer and information sciences