隐私计算与数据要素 · 全部实体（第 7 / 100 页）

本页收录第 601–700 条，共 10000 条 · 返回 隐私计算与数据要素 首页

• Correlated Differential Privacy for Non-IID Datasets
  crossref Tianqing Zhu, Gang Li, Wanlei Zhou, Philip S. Yu 2017-08-22T09:03:04Z 置信度 0.70
• Stability and differential privacy of stochastic gradient methods
  crossref Zhenhuan Yang 2025-05-28T13:58:07Z 置信度 0.70
• Image Privacy Protection Model Based on Differential Privacy and Improved <scp>CIAGAN</scp> Algorithm
  ABSTRACT With the popularity of the Internet and intelligent devices, the privacy protection of image data has become an important problem that needs to be solved urgently. Traditional privacy protection methods such as anonymization have limitations in proces…
  crossref Xue Duan 2026-01-23T09:36:57Z 置信度 0.70
• Estimating Latent Heterogeneity Under Differential Privacy
  This paper studies a simple linear panel model whose coefficients follow multi-dimensional group patterns, with estimates released under unit-level differential privacy. When each combination of parameters is estimated on its own, and some combinations are spa…
  crossref Peng Shao 2026-08-26T17:38:56Z 置信度 0.70
• Random Differential Privacy
  We propose a relaxed privacy definition called {\em random differential privacy} (RDP). Differential privacy requires that adding any new observation to a database will have small effect on the output of the data-release procedure. Random differential privacy …
  crossref Robert Hall, Larry Wasserman, Alessandro Rinaldo 2018-02-27T14:38:30Z 置信度 0.70
• GuardianAI: Privacy-preserving federated anomaly detection with differential privacy
  crossref Abdulatif Alabdulatif 2025-03-05T19:32:37Z 置信度 0.70
• Augmented Differential Privacy Framework for Data Analytics
  Differential privacy has emerged as a popular privacy framework for providing privacy preserving noisy query answers based on statistical properties of databases. It guarantees that the distribution of noisy query answers changes very little with the addition …
  crossref 2024-02-02T08:26:21Z 置信度 0.70
• Federated Naive Bayes under Differential Privacy
  crossref Thomas Marchioro, Lodovico Giaretta, Evangelos Markatos, Šarūnas Girdzijauskas 2022-07-14T20:19:10Z 置信度 0.70
• Prior-itizing Privacy: A Bayesian Approach to Setting the Privacy Budget in Differential Privacy
  crossref Zeki Kazan, Jerome Reiter 2025-11-03T11:20:56Z 置信度 0.70
• Differential privacy for metric spaces : information-theoretic models for privacy and utility with new applications to metric domains
  Confidentialité différentielle pour les espaces métriques : modèles théoriques de l'information pour la confidentialité et l'utilité avec de nouvelles applications aux domaines métriques La "differential privacy", introduite par Dwork et al. en 2006, est deven…
  crossref Natasha Fernandes 2026-04-06T13:49:30Z 置信度 0.70
• Making Privacy Public: Toward a Differential Privacy Deployment Registry
  crossref Priyanka Nanayakkara, Elena Ghazi, Salil Vadhan 2026-07-01T19:34:20Z 置信度 0.70
• Differential Privacy: 6 Key Equations Explained
  Differential Privacy is a powerful framework for ensuring privacy in data analysis by adding controlled noise to computations. Its mathematical foundation guarantees that the presence or absence of any individual's data in a dataset does not significantly affe…
  crossref Abhishek Tiwari 2025-12-06T21:02:23Z 置信度 0.70
• A Quaternion Rotation-Enhanced Differential Privacy Framework for Image Privacy Protection_supp1-3707866.pdf
  crossref Jiaohua Qin 2026-08-12T19:17:04Z 置信度 0.70
• Heterogeneous Differential Privacy
  The massive collection of personal data by personalization systems has rendered the preservation of privacy of individuals more and more difficult. Most of the proposed approaches to preserve privacy in personalization systems usually address this issue unifor…
  crossref Mohammad Alaggan, Sébastien Gambs, Anne-Marie Kermarrec 2018-02-23T21:25:29Z 置信度 0.70
• 7 146Differential Privacy
  crossref 2026-01-19T16:37:33Z 置信度 0.70
• Optimizing Federated Learning with Local Differential Privacy: A Game-Theoretic Approach for Privacy-Utility Tradeoff
  crossref QingKui Zeng, Chunyong Yin 2024-06-11T22:18:26Z 置信度 0.70
• Enhancing Privacy and Utility in Federated Learning: A Hybrid P2P and Server-Based Approach with Differential Privacy Protection
  crossref Luca Corbucci, Anna Monreale, Roberto Pellungrini 2024-07-12T19:48:20Z 置信度 0.70
• Differential Privacy: 6 Key Equations Explained
  Differential Privacy is a powerful framework for ensuring privacy in data analysis by adding controlled noise to computations. Its mathematical foundation guarantees that the presence or absence of any individual's data in a dataset does not significantly affe…
  crossref Abhishek Tiwari 2024-12-01T18:14:36Z 置信度 0.70
• DPolicy: Managing Privacy Risks Across Multiple Releases with Differential Privacy
  crossref Nicolas Küchler, Alexander Viand, Hidde Lycklama, Anwar Hithnawi 2025-06-16T18:46:58Z 置信度 0.70
• Author response for "Protecting health monitoring privacy in fitness training: A federated learning framework based on personalized differential privacy"
  crossref Lifang Shao 2024-05-01T09:59:27Z 置信度 0.70
• Decision letter for "Protecting health monitoring privacy in fitness training: A federated learning framework based on personalized differential privacy"
  crossref 2024-05-01T09:59:27Z 置信度 0.70
• A Weighted Privacy Mechanism under Differential Privacy
  crossref D Hemkumar, Pvn Prashanth 2024-08-15T13:21:37Z 置信度 0.70
• Author response for "Protecting health monitoring privacy in fitness training: A federated learning framework based on personalized differential privacy"
  crossref Lifang Shao 2024-05-01T09:59:27Z 置信度 0.70
• Decision letter for "Protecting health monitoring privacy in fitness training: A federated learning framework based on personalized differential privacy"
  crossref 2024-05-01T09:59:27Z 置信度 0.70
• Decision letter for "Protecting health monitoring privacy in fitness training: A federated learning framework based on personalized differential privacy"
  crossref 2024-05-01T09:59:27Z 置信度 0.70
• Educational Data Privacy: a Differential Privacy Algorithm
  crossref Rafael Russi Zamboni, Itana Stiubiener 2026-01-13T20:56:24Z 置信度 0.70
• Managing Differential Privacy in Large Scale Systems
  The promise of differential privacy is compelling. It offers a rigorous, provable guarantee of individual privacy, even in the face of arbitrary background knowledge. Rather than relying on anonymization techniques that can often be defeated, differential priv…
  crossref Abhishek Tiwari 2024-10-31T18:36:01Z 置信度 0.70
• Privacy-Preserving Technologies in Telecom Data Analytics: Implementing Privacy-Preserving Techniques Like Differential Privacy to Protect Sensitive Customer Data During Telecom Data Analytics&amp;nbsp;
  In an era where data is often referred to as the new oil, the telecommunications industry faces increasing scrutiny over how it handles sensitive customer information. As telecom companies leverage data analytics to enhance services, improve customer experienc…
  crossref Jeevan Kumar Manda 2025-02-14T08:57:18Z 置信度 0.70
• Adding Differential Privacy in an Open Board Discussion Board System
  crossref Pragya Rana 2019-04-18T18:18:18Z 置信度 0.70
• What’s Driving Conflicts Around Differential Privacy for the U.S. Census
  crossref Priyanka Nanayakkara, Jessica Hullman 2022-09-27T19:52:42Z 置信度 0.70
• Information Design for Differential Privacy
  Firms and statistical agencies must protect the privacy of the individuals whose data they collect, analyze, and publish. Increasingly, these organizations do so by using publication mechanisms that satisfy differential privacy. We consider the problem of choo…
  crossref Ian M. Schmutte, Nathan Yoder 2021-03-24T23:03:42Z 置信度 0.70
• Balancing privacy and explainability in AI: Differential privacy and graph theory as governance tools
  Anonymisation (particularly the differential privacy method) stands as the most valuable technique for safeguarding individuals’ privacy, especially in an organisational context. Combining graph theory with the differential privacy method ensures that while da…
  crossref Anna Popowicz-Pazdej 2026-05-31T10:40:00Z 置信度 0.70
• Review for "A Consistent Differential Privacy Dynamic Trajectory Flow Prediction Method"
  crossref 2025-05-16T17:10:17Z 置信度 0.70
• Does Differential Privacy Protect Terry Gross’ Privacy?
  crossref Krish Muralidhar, Rathindra Sarathy 2010-09-14T04:57:53Z 置信度 0.70
• An Integrated Approach of Differential Privacy Using Cryptographic Systems
  crossref Chitra M, Tvisha Prasad, Anshuman Suresh 2025-09-26T09:33:50Z 置信度 0.70
• Large Graph Signal Denoising with Application to Differential Privacy
  Débruitage de signaux définis sur des graphes de grande taille avec application à la confidentialité différentielle Au cours de la dernière décennie, le traitement du signal sur graphe est devenu un domaine de recherche très actif. Plus précisément, le nombre …
  crossref Elie Chedemail 2026-04-08T10:22:05Z 置信度 0.70
• Review for "A Consistent Differential Privacy Dynamic Trajectory Flow Prediction Method"
  crossref 2025-05-16T17:10:17Z 置信度 0.70
• Managing Differential Privacy in Large Scale Systems
  The promise of differential privacy is compelling. It offers a rigorous, provable guarantee of individual privacy, even in the face of arbitrary background knowledge. Rather than relying on anonymization techniques that can often be defeated, differential priv…
  crossref Abhishek Tiwari 2025-12-06T21:02:54Z 置信度 0.70
• Review for "A Consistent Differential Privacy Dynamic Trajectory Flow Prediction Method"
  crossref 2025-05-16T17:10:17Z 置信度 0.70
• On the Differential Privacy of Statistical Analysis in Clinical Studies
  crossref Xiaowen Fu 2024-12-29T23:04:45Z 置信度 0.70
• Differential privacy for survival analysis and user data collection
  crossref Thong T. Nguyen 2019-09-11T04:02:18Z 置信度 0.70
• Review for "A Consistent Differential Privacy Dynamic Trajectory Flow Prediction Method"
  crossref 2025-05-16T17:10:17Z 置信度 0.70
• Bridging the gap: human centered research for democratizing differential privacy
  crossref Liudas Panavas 2025-12-23T14:46:57Z 置信度 0.70
• Evaluating Synthetic Data Generators in the Context of Differential Privacy
  crossref Andrea Galloni 2026-05-21T08:23:13Z 置信度 0.70
• Inductive learning and local differential privacy for privacy-preserving offloading in mobile edge intelligent systems
  We address privacy and latency issues in the edge/cloud computing environment while training a centralized AI model. In our particular case, the edge devices are the only data source for the model to train on the central server. Current privacy-preserving and …
  crossref Jude TCHAYE-KONDI, Yanlong Zhai, Liehuang Zhu 2021-02-06T04:13:02Z 置信度 0.70
• Privacy-Preserving AI: Unlocking Data Potential through Differential Privacy
  As data-driven technologies continue to evolve, ensuring the privacy of individuals has become a fundamental challenge. This paper explores the transformative role of differential privacy in enabling privacy-preserving artificial intelligence. By incorporating…
  crossref Muhammad Shees Shoaib 2026-08-25T14:01:56Z 置信度 0.70
• Differential Privacy Approach for Big Data Privacy in Healthcare
  This chapter presents a survey of the most important security and privacy issues related to large-scale data sharing and mining in big data with focus on differential privacy as a promising approach for achieving privacy especially in statistical databases oft…
  crossref Marmar Moussa, Steven A. Demurjian 2017-03-03T11:20:29Z 置信度 0.70
• Budget Recycling Differential Privacy
  crossref Bo Jiang, Jian Du, Sagar Sharma, Qiang Yan 2024-09-05T17:56:32Z 置信度 0.70
• Between Privacy and Utility: On Differential Privacy in Theory and Practice
  crossref Jeremy Seeman, Daniel Susser 2022-12-06T05:04:28Z 置信度 0.70
• Manipulation Attacks in Local Differential Privacy
  Local differential privacy is a widely studied restriction on distributed algorithms that collect aggregates about sensitive user data, and is now deployed in several large systems. We initiate a systematic study of a fundamental limitation of locally differen…
  crossref Albert Cheu, Adam Smith, Jonathan Ullman 2021-02-04T01:01:19Z 置信度 0.70
• PA-FedVL: A Privacy-Enhanced Federated Virtual Learning Framework Based on Label Differential Privacy Distillation
  crossref YU YuanXi, Wang Ying, GONG Bo 2026-01-26T12:37:05Z 置信度 0.70
• Inductive learning and local differential privacy for privacy-preserving offloading in mobile edge intelligent systems
  &lt;div&gt;We address privacy and latency issues in the edge/cloud computing environment while training a centralized AI model. In our particular case, the edge devices are the only data source for the model to train on the central server. Current privacy-pres…
  crossref Jude TCHAYE-KONDI, Yanlong Zhai, Liehuang Zhu 2021-02-06T09:13:01Z 置信度 0.70
• Inductive learning and local differential privacy for privacy-preserving offloading in mobile edge intelligent systems
  We address privacy and latency issues in the edge/cloud computing environment while training a centralized AI model. In our particular case, the edge devices are the only data source for the model to train on the central server. Current privacy-preserving and …
  crossref Jude TCHAYE-KONDI, Yanlong Zhai, Liehuang Zhu 2021-02-11T16:42:57Z 置信度 0.70
• Dpro-Gnn: Bridging Differential Privacy and Advanced Optimization for Privacy-Preserving Graph Learning
  crossref Yanan Bai, Liji Xiao, Hongbo Zhao, Xiaoyu Shi 2025-03-15T23:38:56Z 置信度 0.70
• Manipulation Attacks in Local Differential Privacy
  crossref Albert Cheu, Adam Smith, Jonathan Ullman 2021-08-26T17:03:31Z 置信度 0.70
• Coded Computing Meets Differential Privacy: Privacy-Preserving and Straggler-Resilient Distributed Machine Learning_supp1-3669529.pdf
  crossref Jun Wu 2026-03-05T20:47:03Z 置信度 0.70
• Differential Privacy Preserving Causal Graph Discovery
  crossref Depeng Xu, Shuhan Yuan, Xintao Wu 2017-12-07T23:28:48Z 置信度 0.70
• Tight Bounds for Machine Unlearning via Differential Privacy
  We consider the formulation of ``machine unlearning'' of Sekhari, Acharya, Kamath, and Suresh (NeurIPS 2021), which formalizes the so-called ``"right to be forgotten" by requiring that a trained model, upon request, should be able to 'unlearn' a number of poin…
  crossref Yiyang Huang, Clement Canonne 2025-09-01T01:13:20Z 置信度 0.70
• Risks of Privacy-Enhancing Technologies: Complexity and Implications of Differential Privacy in the Context of Cybercrime
  crossref William Stadler 2020-09-09T07:16:00Z 置信度 0.70
• A Privacy Protection Scheme to Trajectory Synthesis Using Differential Privacy
  crossref 2025-08-29T04:31:06Z 置信度 0.70
• Lightweight Crypto-Assisted Distributed Differential Privacy for Privacy-Preserving Distributed Learning
  crossref Lingjuan Lyu 2020-09-29T20:40:33Z 置信度 0.70
• Differential Privacy Techniques in Healthcare Machine Learning
  The integration of machine learning (ML) in healthcare promises unprecedented advancements in diagnostic accuracy, personalized treatment, and operational efficiency. However, the sensitive nature of medical data necessitates robust privacy-preserving mechanis…
  crossref Emily Wilson, Ficek Josep 2026-04-14T08:15:42Z 置信度 0.70
• The ε Dilemma Balancing Privacy and Utility in Differential Privacy
  crossref 2026-02-27T08:16:23Z 置信度 0.70
• The Algorithmic Foundations of Differential Privacy
  crossref Cynthia Dwork, Aaron Roth 2020-02-26T02:43:35Z 置信度 0.70
• 𝜖-Differential Privacy
  crossref 2020-09-14T10:42:37Z 置信度 0.70
• Dynamic-Differential Privacy based on Feature Selection with Improved Usability and Security
  crossref Sun-Jin Lee, Hye-Yeon Shim, Jung-Hwa Rye, Il-Gu Lee 2025-02-25T06:22:45Z 置信度 0.70
• Real-time privacy-preserving threat detection in IoT environments using federated learning and differential privacy
  crossref 2025-09-15T17:39:13Z 置信度 0.70
• On sampling, anonymization, and differential privacy or, <i>k</i> -anonymization meets differential privacy
  crossref Ninghui Li, Wahbeh Qardaji, Dong Su 2012-12-17T10:12:21Z 置信度 0.70
• Privacy-Enhanced and Multifunctional Health Data Aggregation under Differential Privacy Guarantees
  With the rapid growth of the health data scale, the limited storage and computation resources of wireless body area sensor networks (WBANs) is becoming a barrier to their development. Therefore, outsourcing the encrypted health data to the cloud has been an ap…
  crossref Hao Ren, Hongwei Li, Xiaohui Liang, Shibo He 等 2021-07-28T07:33:50Z 置信度 0.70
• Chapter 5 Privacy Risks in Machine Learning
  crossref Jiayuan Ye, Reza Shokri 2025-07-23T08:26:52Z 置信度 0.70
• Differential Privacy in Practice: Expose your Epsilons!
  Differential privacy is at a turning point. Implementations have been successfully leveraged in private industry, the public sector, and academia in a wide variety of applications, allowing scientists, engineers, and researchers the ability to learn about popu…
  crossref Cynthia Dwork, Nitin Kohli, Deirdre Mulligan 2019-10-23T20:27:45Z 置信度 0.70
• Differential Privacy on Finite Computers
  We consider the problem of designing and analyzing differentially private algorithms that can be implemented on discrete models of computation in strict polynomial time, motivated by known attacks on floating point implementations of real-arithmetic differenti…
  crossref Victor Balcer, Salil Vadhan 2019-10-23T16:27:45Z 置信度 0.70
• The Complexities of Differential Privacy for Survey Data
  crossref Jörg Drechsler, James Bailie 2024-09-09T23:31:53Z 置信度 0.70
• ε-Differential Privacy, and a Two Step
  Sharing data in the 21st century is fraught with error. Most commonly, data is freely accessible, surreptitiously stolen, and easily capitalized in the pursuit of monetary maximization. But when data does find itself shrouded behind the veil of “personally ide…
  crossref Nathan Reitinger, Amol Deshpande 2022-12-24T07:14:54Z 置信度 0.70
• Differential Privacy for Databases
  crossref Joseph P. Near, Xi He 2021-07-29T23:15:21Z 置信度 0.70
• A Community‐Driven Differential Privacy Deployment Registry
  crossref Gary Howarth 2025-09-10T12:50:11Z 置信度 0.70
• Special Issue on the Theory and Practice of Differential Privacy 2016
  crossref Marco Gaboardi 2019-04-01T02:10:17Z 置信度 0.70
• Privacy-Enhancing Machine Learning with Differential Privacy
  Privacy concerns in machine learning have become increasingly critical as AI systems process sensitive personal, financial, healthcare, and behavioral data. Traditional machine learning approaches often require centralizing large datasets, raising risks of dat…
  crossref STEPHEN ETENG 2026-03-20T08:04:34Z 置信度 0.70
• Differential Performance and Contextual Integrity in Speech Privacy
  crossref Poppy Welch, Natalia Tomashenko, Sneha Das, Jennifer Williams 2026-04-16T19:55:33Z 置信度 0.70
• Enhancing Privacy in Recommender Systems through Differential Privacy Techniques
  crossref Angela Di Fazio 2024-10-08T15:39:28Z 置信度 0.70
• Privacy Bargaining with Fairness: Privacy-Price Negotiation System for Applying Differential Privacy in Data Market Environments
  crossref Kangsoo Jung, Seog Park 2020-02-25T06:05:34Z 置信度 0.70
• A Quaternion Rotation-Enhanced Differential Privacy Framework for Image Privacy Protection.
  europepmc 2026 置信度 0.80
• Enhancing face recognition privacy through the integration of differential privacy and convolutional neural network.
  europepmc 2026 置信度 0.80
• Information-Theoretic Limits and One-Bit Estimation of Sparse Relation Matrices Under Personalized Local Differential Privacy and Byzantine Contamination
  europepmc 2026 置信度 0.80
• Efficient Data Aggregation in Smart Grids: A Personalized Local Differential Privacy Scheme.
  europepmc 2026 置信度 0.80
• Verifiable Differential Privacy Partial Disclosure for IoT with Stateless k-Use Tokens.
  europepmc 2026 置信度 0.80
• Preface
  crossref 2025-03-25T16:46:09Z 置信度 0.70
• Introduction
  crossref 2025-03-25T16:46:09Z 置信度 0.70
• Differential Privacy
  crossref 2023-10-26T00:05:54Z 置信度 0.70
• Privacy Preservation in Distributed Healthcare Systems: A Review of Differential Privacy, Homomorphic Encryption, and Hybrid Approaches
  crossref K Mahesh Babu, M V S S Nagendranath 2025-10-20T17:48:22Z 置信度 0.70
• Homomorphic Encryption, Its Schemes and Libraries: A Mini Review
  Homomorphic encryption is an innovative cryptographic technique that allows operation on ciphertexts without decrypting them. Such encryption allows data to be handled directly in its encrypted state while maintaining confidentiality, and it finds particular u…
  crossref Olabode Idowu-Bismark, Nicol Ituh, Kennedy Okokpujie, Oluwadamilola Oshin 等 2026-01-24T12:45:18Z 置信度 0.70
• Review of How to Construct a Fully Homomorphic Encryption Scheme
  crossref Zhi-gang Chen, Jian Wang, Liqun Chen, Xin-xia Song 2015-08-26T01:42:41Z 置信度 0.70
• Limitations on information-theoretically-secure quantum homomorphic encryption
  crossref Li Yu, Carlos A. Pérez-Delgado, Joseph F. Fitzsimons 2014-11-10T17:08:56Z 置信度 0.70
• A critical analysis on homomorphic searchable encryption architectures and security models over multi-medium data
  crossref Aiman Sultan, Tayyaba Anwer, Shahzaib Tahir, Hasan Tahir 等 2026-08-26T09:06:17Z 置信度 0.70
• Review on Cloud Data Security Using VGG19-Deep Learning and Homomorphic Encryption
  Data is the new currency as lot of the user’s presence online is an upward trend. As a consequence the data storage on the various cloud platforms has been a new normal. Data security in cloud has turn formidable due to unique security issues and challenges. C…
  crossref Chandrasekhar Tadi., Basanta Th., Swaminathan J.N. 2025-11-19T05:24:20Z 置信度 0.70
• High-Throughput Key Switching Accelerator for Homomorphic Encryption
  crossref Zeyu Wang, Makoto Ikeda 2023-12-06T18:24:27Z 置信度 0.70
• Resilient Homomorphic Encryption Scheme for Cyber-Physical Systems
  crossref Moritz Fauser, Ping Zhang 2022-02-01T15:50:18Z 置信度 0.70
• A systematic review of homomorphic encryption techniques to preserve confidentiality in cloud environment
  crossref Krishnakumar Durai, Ramkumar Ketti Ramachandran, Sonam Mittal 2024-10-11T17:00:49Z 置信度 0.70
• HEDPF: A Hybrid Privacy-Preserving Framework Integrating Homomorphic Encryption and Differential Privacy for Big Data Information Systems
  Privacy protection has become a critical challenge in big data information systems due to the exponentially increasing volume, diversity, and sensitivity of digital data. Traditional privacy-preserving methods often suffer from limited security guarantees, sig…
  crossref Hua Xu 2026-06-26T05:53:50Z 置信度 0.70
• Security Sharing Strategy for Financial Data Based on Homomorphic Encryption
  crossref Nannan Sun 2026-05-06T22:33:20Z 置信度 0.70