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• 3I/ATLAS and the Resolution of All Its Mysteries through the 165-Dimensional Tensor Mechanics of the Hamzah Equation.
  3I/ATLAS and the Resolution of All Its Mysteries through the 165-Dimensional Tensor Mechanics of the Hamzah Equation. ..............................................................................................................................................…
  datacite JALALI, SEYED RASOUL 2026 置信度 0.66
• Tensor Mechanics and Mathematics of Hamzah 165-D
  An Introduction to Hamza’s 165-Dimensional Tensor Mathematics ($165\text{D-HTM}$) From the Darkness of Numbers to the Radiance of Manifestation 1. The Twilight of Classical Geometry For millennia, human mathematics remained incarcerated within the prison of La…
  datacite JALALI, SEYED RASOUL 2026 置信度 0.66
• Tensor Mechanics and Mathematics of Hamzah 165-D
  An Introduction to Hamza’s 165-Dimensional Tensor Mathematics ($165\text{D-HTM}$) From the Darkness of Numbers to the Radiance of Manifestation 1. The Twilight of Classical Geometry For millennia, human mathematics remained incarcerated within the prison of La…
  datacite JALALI, SEYED RASOUL 2026 置信度 0.66
• Differentially Private Adaptation of Diffusion Models via Noisy Aggregated Embeddings
  Personalizing large-scale diffusion models poses serious privacy risks, especially when adapting to small, sensitive datasets. A common approach is to fine-tune the model using differentially private stochastic gradient descent (DP-SGD), but this suffers from …
  datacite Peetathawatchai, Pura, Chen, Wei-Ning, Isik, Berivan, Koyejo, Sanmi 等 2024 置信度 0.66 Computer Vision and Pattern Recognition (cs.CV)Cryptography and Security (cs.CR)Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• Refining impact assessment in undergraduate STEM education: Differential item functioning analysis of field-based learning interventions
  This dataset contains self-efficacy survey responses from undergraduate biology students enrolled in three different course formats (lecture, introductory field, and intensive field) at the University of California, Santa Cruz from 2016-2019. The data structur…
  datacite Bhatti, Haider Ali, Arcila Hernández, Lina, Balachandran, Lalitha, Kouba, Paige 等 2025 置信度 0.66 FOS: Educational sciencesFOS: Educational sciencesfield biologyfield courseinquiry-based learning
• PRIV-ML: Analyzing Privacy Loss in Iterative Machine Learning with Differential Privacy
  crossref Pratik Thantharate, Divya Ananth Todurkar, Anurag T 2024-08-14T17:31:20Z 置信度 0.70
• A differential privacy aided DeepFed intrusion detection system for IoT applications
  Abstract In the rapidly‐developing Internet of Things (IoT) ecosystem, safeguarding the privacy and accuracy of linked devices and networks is of utmost importance, with the challenge lying in effective implementation of intrusion detection systems on resource…
  crossref Sayeda Suaiba Anwar, Asaduzzaman, Iqbal H. Sarker 2024-07-11T03:09:27Z 置信度 0.70
• Privacy-First Crowdsourcing: Blockchain and Local Differential Privacy in Crowdsourced Drone Services
  crossref Junaid Akram, Ali Anaissi 2024-11-06T18:36:03Z 置信度 0.70
• The Complexities of Differential Privacy for Survey Data
  The concept of differential privacy (DP) has gained substantial attention in recent years, most notably since the U.S. Census Bureau announced the adoption of the concept for its 2020 Decennial Census. However, despite its attractive theoretical properties, im…
  crossref Jörg Drechsler, James Bailie 2024-09-10T05:43:19Z 置信度 0.70
• Generalized Differential Privacy for Clustering
  In distributed data analysis, clustering plays a vital role in uncovering patterns and relationships across datasets. However, ensuring individual privacy in decentralized settings remains a challenge. Existing approaches often fall short in protecting individ…
  crossref Mina Alishahi, Tjibbe Van Der Ende, Clara Maathuis 2024-08-26T12:10:20Z 置信度 0.70
• Distributed Differential Privacy for Federated Learning: A Privacy-Enhancing Approach
  crossref Hanlei Zhou, Jie Kong 2025-03-04T13:39:08Z 置信度 0.70
• Density-Based Clustering with Differential Privacy
  crossref Fuyu Wu, Mingjing Du, Qiang Zhi 2024-04-18T03:50:50Z 置信度 0.70
• Lower Bounds for Rényi Differential Privacy in a Black-Box Setting
  crossref Tim Kutta, Önder Askin, Martin Dunsche 2024-09-05T17:56:32Z 置信度 0.70
• Cohere: Managing Differential Privacy in Large Scale Systems
  crossref Nicolas Küchler, Emanuel Opel, Hidde Lycklama, Alexander Viand 等 2024-09-05T17:56:32Z 置信度 0.70
• Efficient federated learning privacy preservation method with heterogeneous differential privacy
  crossref Jie Ling, Junchang Zheng, Jiahui Chen 2024-01-21T00:24:11Z 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 2021-07-28T07:33:53Z 置信度 0.70
• Pk-Anonymization Meets Differential Privacy
  crossref Masaya Kobayashi, Atsushi Fujioka, Koji Chida, Akira Nagai 等 2024-12-16T19:15:09Z 置信度 0.70
• Optimization of Privacy Budget Allocation in Differential Privacy Technology: An Empirical Study
  crossref Yunfei Liu 2024-10-08T16:36:10Z 置信度 0.70
• DP-Auditorium: A Large-Scale Library for Auditing Differential Privacy
  crossref William Kong, Andrés Muñoz Medina, Mónica Ribero, Umar Syed 2024-09-05T17:56:32Z 置信度 0.70
• Preserving Privacy of IoT Healthcare Data using Differential Privacy and LSTM
  The Internet of Things (IoT) is a powerful technology creating revolutions in multiple industries for ex: Traffic and Healthcare domains. The patient data collected by continuous monitoring using IoT will support in treating the patients and make a positive im…
  crossref D Kavitha 2024-05-29T08:53:22Z 置信度 0.70
• Implementation of Differential Privacy for Enhancing Data Privacy in Wearable Fitness Trackers
  crossref Hammad Shaikh, Shariq Mahmood Khan 2025-01-10T20:15:11Z 置信度 0.70
• Eureka: A General Framework for Black-box Differential Privacy Estimators
  crossref Yun Lu, Malik Magdon-Ismail, Yu Wei, Vassilis Zikas 2024-09-05T17:56:32Z 置信度 0.70
• Privacy-Preserving Deep Reinforcement Learning based on Differential Privacy
  crossref Wenxu Zhao, Yingpeng Sang, Neal Xiong, Hui Tian 2024-09-09T17:35:05Z 置信度 0.70
• Exploring the Limits of Differential Privacy
  Differential Privacy (DP) is a powerful technology, but not well-suited to protecting corporate proprietary information while computing aggregate industry-wide statistics. We elucidate this scenario with an example of cybersecurity management data, and conside…
  crossref David D. Clark, Simson Garfinkel, KC C. Claffy 2024-08-01T08:31:58Z 置信度 0.70
• Local differential privacy federated learning based on heterogeneous data multi-privacy mechanism
  crossref Jie Wang, Zhiju Zhang, Jing Tian, Hongtao Li 2024-09-26T02:28:29Z 置信度 0.70
• Density-Based Clustering with Differential Privacy
  crossref Fuyu Wu, Mingjing Du, Qiang Zhi, Jiarui Sun 2024-02-04T11:18:34Z 置信度 0.70
• Privacy Protection in Mobile Crowdsensing Based on Local Differential Privacy
  crossref Xinyue Cui, Pengxuan Sun, Ningning Guo 2025-02-22T11:22:41Z 置信度 0.70
• Casual Users and Rational Choices within Differential Privacy
  crossref Narges Ashena, Oana Inel, Badrie L. Persaud, Abraham Bernstein 2024-09-05T17:56:32Z 置信度 0.70
• Reputation-Based Resilient Consensus with Differential Privacy Guarantees
  crossref Guilherme Ramos, Sérgio Pequito, Daniel Silvestre 2024-02-21T06:20:27Z 置信度 0.70
• Estimating the Privacy Budget for Differential Privacy by Machine Learning
  crossref Ying-Hsuan Wang, Shih-Hsuan Yang, Yu-Chi Chen 2024-09-18T17:51:53Z 置信度 0.70
• Enhancing Privacy and Security in Recommender Systems Through Federated Learning and Differential Privacy
  crossref Zhigang Yang, Tafadzwa Mbodza 2025-01-22T18:47:22Z 置信度 0.70
• Enhancing Trajectory Privacy with a Differential Obfuscation Mechanism
  crossref Yu-Ning Fang, Yu-Ling Hsueh 2024-10-19T23:38:11Z 置信度 0.70
• Blockchain-Enabled Student Privacy Protection in Virtual Collaborative Learning Environments: A Novel Approach Using Localized Differential Privacy and Attribute-Based Searchable Encryption
  crossref Lin Jia 2024-03-25T18:15:41Z 置信度 0.70
• SPM-FL: A Federated Learning Privacy-Protection Mechanism Based on Local Differential Privacy
  Federated learning is a widely applied distributed machine learning method that effectively protects client privacy by sharing and computing model parameters on the server side, thus avoiding the transfer of data to third parties. However, information such as …
  crossref Zhiyan Chen, Hong Zheng 2024-10-17T08:56:32Z 置信度 0.70
• Research on differential privacy protection algorithm for federated learning based on user privacy requirements
  crossref Meijiao Wu 2025-01-14T19:40:10Z 置信度 0.70
• INTEGRATING DIFFERENTIAL PRIVACY WITH BLOCKCHAIN FOR PRIVACY-PRESERVING RECOMMENDATION SYSTEMS
  Recommendation systems have become an integral part of the digital landscape, powering personalized experiences and driving engagement across a wide range of industries. However, traditional recommendation systems face significant challenges, including data pr…
  crossref Isabella Fernandez, Aditya Raghavan 2024-11-30T04:14:44Z 置信度 0.70
• Caldp: A Correlation-Aware Approach to Local Differential Privacy
  crossref Majid Zolfaghari, Ahmad Mohammadi, Rasool Jalili 2024-06-18T01:17:38Z 置信度 0.70
• Evaluating Differential Privacy on Correlated Datasets Using Pointwise Maximal Leakage
  crossref Sara Saeidian, Tobias J. Oechtering, Mikael Skoglund 2024-07-31T16:07:34Z 置信度 0.70
• On the Connection Between the ABS Perturbation Methodology and Differential Privacy
  This paper explores analytical connections between the perturbation methodology of the Australian Bureau of Statistics (ABS) and the differential privacy (DP) framework. We consider a single static counting query function and find the analytical form of the pe…
  crossref Parastoo Sadeghi, Chien-Hung Chien 2024-06-24T13:10:43Z 置信度 0.70
• Enhancing Image Privacy in Semantic Communication over Wiretap Channels Leveraging Differential Privacy
  crossref Weixuan Chen, Shunpu Tang, Qianqian Yang 2024-11-04T18:31:55Z 置信度 0.70
• Computational Differential Privacy for Encrypted Databases Supporting Linear Queries
  Differential privacy is a fundamental concept for protecting individual privacy in databases while enabling data analysis. Conceptually, it is assumed that the adversary has no direct access to the database, and therefore, encryption is not necessary. However,…
  crossref Ferran Alborch Escobar, Sébastien Canard, Fabien Laguillaumie, Duong Hieu Phan 2024-07-06T15:52:08Z 置信度 0.70
• Privacy-Preserving Gross Domestic Product (GDP) Calculation Using Paillier Encryption and Differential Privacy
  crossref Sanjaikanth E. Vadakkethil Somanathan Pillai, Wen-Chen Hu 2024-04-27T12:06:34Z 置信度 0.70
• Integrating Differential Privacy in Modern Database Curriculum
  crossref Jeff Miller, Ankur Chattopadhyay 2024-09-17T18:46:44Z 置信度 0.70
• Enhancing Privacy Protection for Federated Learning with Distributed Differential Privacy
  crossref Wenjing Wei, Alla Jammine, Farid Nait-Abdesselam 2025-01-10T19:56:17Z 置信度 0.70
• Optimizing Privacy in Federated Learning with MPC and Differential Privacy
  crossref Chao Zheng, Liming Wang, Zhen Xu, Hongjia Li 2024-05-29T16:20:33Z 置信度 0.70
• Balancing Privacy and Performance: A Differential Privacy Approach in Federated Learning
  Federated learning (FL), a decentralized approach to machine learning, facilitates model training across multiple devices, ensuring data privacy. However, achieving a delicate privacy preservation–model convergence balance remains a major problem. Understandin…
  crossref Huda Kadhim Tayyeh, Ahmed Sabah Ahmed AL-Jumaili 2024-10-24T05:47:33Z 置信度 0.70
• Data Privacy Preservation Using Differential Privacy and Re-Identification Attacks
  crossref G. Sathish Kumar, K Preethie, S Madhumitha, R Sushma 等 2024-06-25T19:20:04Z 置信度 0.70
• Protecting health monitoring privacy in fitness training: A federated learning framework based on personalized differential privacy
  Abstract The rapid advancement of health monitoring technologies has led to increased adoption of fitness training applications that collect and analyze personal health data. This paper presents a personalized differential privacy‐based federated learning (PDP…
  crossref Lifang Shao 2024-01-07T20:58:50Z 置信度 0.70
• Improved Data Privacy with Differential Privacy in Federated Learning
  Multiple users may train machine learning models cooperatively using Federated Learning (FL). There is a risk of malicious acquisition of participants' personal data due to the fact that traditional machine learning needs users to provide data for training. Th…
  crossref Cina Mathew, Dr.P. Asha 2024-10-08T09:35:18Z 置信度 0.70
• Privacy in manifolds: Combining k-anonymity with differential privacy on Fréchet means
  crossref Sonakshi Garg, Vicenç Torra 2024-07-06T02:43:26Z 置信度 0.70
• Investigating the Impact of Differential Privacy Obfuscation on Users' Data Disclosure Decisions
  crossref Michael Khavkin, Eran Toch 2024-06-04T20:49:01Z 置信度 0.70
• Efficiency in local differential privacy
  crossref Lukas Steinberger 2024-11-21T06:27:28Z 置信度 0.70
• Concentrated Differential Privacy for Bandits
  crossref Achraf Azize, Debabrota Basu 2024-05-10T17:22:05Z 置信度 0.70
• Privacy Protection in Trajectory Data Publication Based on Differential Privacy
  crossref Xiujun Wang, Qing Gao, Xiao Zheng, Tao Tao 等 2025-03-11T17:30:35Z 置信度 0.70
• Adaptive Personalized Randomized Response Method Based on Local Differential Privacy
  Aiming at the problem of adopting the same level of privacy protection for sensitive data in the process of data collection and ignoring the difference in privacy protection requirements, the authors propose an adaptive personalized randomized response method …
  crossref Dongyan Zhang, Lili Zhang, Zhiyong Zhang, Zhongya Zhang 2024-01-10T11:53:45Z 置信度 0.70
• Towards Assessing Integrated Differential Privacy and Fairness Mechanisms in Supervised Learning
  crossref Maryam Aldairi, James Joshi 2025-01-16T18:34:22Z 置信度 0.70
• Balancing Privacy and Accuracy: Federated Learning with Differential Privacy for Medical Image Data
  crossref Muhammad Hamza Mehmood, Mahnoor Iqbal Khan, Abdulsamad Ibrahim 2025-02-18T18:18:17Z 置信度 0.70
• Privacy-preserving estimation of electric vehicle charging behavior: A federated learning approach based on differential privacy
  crossref Xiuping Kong, Lin Lu, Ke Xiong 2024-08-28T22:42:04Z 置信度 0.70
• MPLDP: Multi-Level Personalized Local Differential Privacy Method
  crossref Xuejie Feng, Chiping Zhang 2024-07-18T17:38:38Z 置信度 0.70
• Instance-Specific Asymmetric Sensitivity in Differential Privacy
  crossref David Durfee 2025-11-03T11:20:56Z 置信度 0.70
• A Secure and Privacy-Preserving Framework for Healthcare Data Management Using Deterministic Additive Noise and Differential Privacy in Cloud Environments
  crossref Yousef Alsaud, Danda B Rawat 2025-01-16T18:31:23Z 置信度 0.70
• Privacy Protection Scheme for Cyberspace Mapping Data Based on Differential Privacy
  crossref Yue Zhang, Guanlin Si, Bin Dong, Leran Chen 等 2024-11-04T18:31:47Z 置信度 0.70
• Bridging Quantum Computing and Differential Privacy: Insights into Quantum Computing Privacy
  crossref Yusheng Zhao, Hui Zhong, Xinyue Zhang, Yuqing Li 等 2025-01-10T20:12:43Z 置信度 0.70
• A Method for Protecting the Privacy of Massive Data Based on Differential Privacy
  crossref Haitao Yu, Yifan Sun, Junyi Xie, Yongdi Bao 等 2025-02-06T18:32:50Z 置信度 0.70
• Privacy in Motion: Implementing Differential Privacy for User Motion in VR
  crossref Ruoxi Sun, Hanwen Wang, Hsiang-Ting Chen, Minhui Xue 2025-09-29T15:13:01Z 置信度 0.70
• A Survey of Privacy Preserving Methods based on Differential Privacy for Medical Data
  crossref Haicao Yan, Menghan Yin, Chaokun Yan, Wenjuan Liang 2024-06-03T17:28:14Z 置信度 0.70
• Balancing Between Privacy and Utility for Affect Recognition using Multi Task Learning in Differential Privacy Added Federated Learning Settings (Preprint)
  BACKGROUND The rise of wearable sensors marks a pivotal development in the era of affective computing. These sensors, gaining increasing popularity, hold the potential to revolutionize our understanding of human stress. A fundamental aspect within this domain …
  crossref Mohamed Benouis, Elisabeth Andre, Yekta CAN 2024-04-29T13:42:56Z 置信度 0.70
• Privacy-Preserving Social Network Clustering Using Differential Privacy
  crossref K Sathiyapriya, R Kavin Aravindhan, B Kireshvanth, Yadav Ranganathan 等 2024-09-03T17:19:41Z 置信度 0.70
• Location Privacy Protection and 911 Task Allocation in Vehicle-Based via Differential Privacy
  crossref Deyuan Qu, Dominic Carrillo, Sudip Dhakal, Mohammad Dehghani Tezerjani 等 2025-01-10T19:44:30Z 置信度 0.70
• ULDP: A User-Centric Local Differential Privacy Optimization Method
  crossref Wenjun Yang, Eyhab Al-Masri 2024-07-10T17:21:40Z 置信度 0.70
• Privacy-preserving Searchable Encryption Based on Anonymization and Differential privacy
  crossref Caixia Ma, Chunfu Jia, Ruizhong Du, Guanxiong Ha 等 2024-10-15T17:19:18Z 置信度 0.70
• Preserving Medical Data with Renyi Differential Privacy
  crossref Olusola Tolulope Odeyomi, Harshitha Karnati, Austin Smith 2024-07-10T17:21:40Z 置信度 0.70
• Enhancing Client Privacy in Physiology-Based Biometric Verification with Differential Privacy and Positive-Label Federated Learning
  crossref Mohamed Benouis, Bhargavi Mahesh, Elisabeth André, Yekta Said Can 2024-07-22T17:31:28Z 置信度 0.70
• Differential Privacy in Nonlinear Dynamical Systems with Tracking Performance Guarantees
  crossref Dhrubajit Chowdhury, Raman Goyal, Shantanu Rane 2024-09-05T17:56:19Z 置信度 0.70
• Optimizing Image Recognition Algorithms with Differential Privacy Integration
  crossref Shaoyu Yang 2024-06-26T08:03:44Z 置信度 0.70
• DP-EPSO: Differential privacy protection algorithm based on differential evolution and particle swarm optimization
  crossref Qiang Gao, Han Sun, Zhifang Wang 2024-01-09T15:18:33Z 置信度 0.70
• Data Level Privacy Preserving: A Stochastic Perturbation Approach Based on Differential Privacy (Extended abstract)
  crossref Chuan Ma, Long Yuan, Li Han, Ming Ding 等 2024-07-23T17:38:03Z 置信度 0.70
• Anti‐Leakage Method of Sensitive Information of Network Documents Based on Differential Privacy Model
  ABSTRACT This article proposes a method for preventing sensitive information leakage in network documents based on differential privacy models to ensure the security of sensitive information in network documents. Based on the definition of differential privacy…
  crossref Shuhui Su, Yonghan Luo, Tao Li, Qi Chen 等 2024-12-24T10:09:01Z 置信度 0.70
• Privacy-preserving Federated Learning for Industrial Defect Detection Systems via Differential Privacy and Image Obfuscation
  crossref Chia-Yu Lin, Yu-Chen Yeh, Makena Lu 2024-07-30T17:50:37Z 置信度 0.70
• Whispered Tuning: Data Privacy Preservation in Fine-Tuning LLMs through Differential Privacy
  crossref Tanmay Singh, Harshvardhan Aditya, Vijay K. Madisetti, Arshdeep Bahga 2024-01-22T03:06:08Z 置信度 0.70
• An Efficient Data Privacy Protection System Based on Differential Privacy
  crossref D. Vetrithangam 2024-01-02T23:02:06Z 置信度 0.70
• 1-Diffractor: Efficient and Utility-Preserving Text Obfuscation Leveraging Word-Level Metric Differential Privacy
  crossref Stephen Meisenbacher, Maulik Chevli, Florian Matthes 2024-06-11T00:20:03Z 置信度 0.70
• FedCRM: Privacy-Preserving Federated Learning for Enterprise Salesforce CRM Analytics with Heterogeneous Schema Support and Differential Privacy
  Enterprise Salesforce CRM implementations across business units, subsidiaries, and partner organisations contain customer relationship data whose analytical value substantially exceeds what any individual CRM yields from isolated local analysis. However, data …
  crossref Lalith Chandra Bandaru 2026-05-27T13:10:51Z 置信度 0.70
• DIFFERENTIAL PRIVACY IN BIG DATA ANALYTICS: TECHNIQUES AND APPLICATIONS
  Differential privacy (DP) is a powerful privacy-preserving technique aimed at protecting individual data in statistical analyses. With the proliferation of big data in diverse sectors such as healthcare, finance, and social media, ensuring privacy without sacr…
  crossref Dr. Amina Farooq 2026-04-11T09:25:35Z 置信度 0.70
• Visualizing Differential Privacy: Assessing Infographics' Impact on Layperson Data-sharing Decisions and Comprehension
  crossref Mst Mahamuda Sarkar Mithila, Fangyi Yu, Miguel Vargas Martin, Shengqian Wang 2024-12-16T19:15:09Z 置信度 0.70
• Privacy-Preserving Graph Embedding based on Local Differential Privacy
  crossref Zening Li, Rong-Hua Li, Meihao Liao, Fusheng Jin 等 2024-10-20T19:34:21Z 置信度 0.70
• Federated learning with hybrid differential privacy for secure and reliable <scp>cross‐IoT</scp> platform knowledge sharing
  Abstract The federated learning has gained prominent attention as a collaborative machine learning method, allowing multiple users to jointly train a shared model without directly exchanging raw data. This research addresses the fundamental challenge of balanc…
  crossref Oshamah Ibrahim Khalaf, Ashokkumar S.R, Sameer Algburi, Anupallavi S 等 2024-02-03T05:35:03Z 置信度 0.70
• A trajectory protection method based on differential privacy and semantic attributes
  crossref 2024-05-24T00:46:41Z 置信度 0.70
• Personalized trajectory differential privacy protection mechanism based on spatiotemporal correlation prediction
  crossref 2024-02-27T00:38:33Z 置信度 0.70
• Mechanisms for Global Differential Privacy under Bayesian Data Synthesis
  crossref Jingchen Hu, Matthew R. Williams, Terrance D. Savitsky 2023-08-15T06:07:57Z 置信度 0.70
• STUDY ON DIFFERENTIAL PRIVACY WITH MACHINE LEARNING
  crossref 2024-01-19T19:47:11Z 置信度 0.70
• AI Model Training Data Privacy Protection Scheme Based on Local Differential Privacy
  crossref Yue Zhang, Lin Li, Cong Hou, Min Li 等 2025-02-11T18:21:13Z 置信度 0.70
• A secure and privacy preserved infrastructure for VANETs based on federated learning with local differential privacy
  crossref Hajira Batool, Adeel Anjum, Abid Khan, Stefano Izzo 等 2023-10-10T13:05:13Z 置信度 0.70
• Escalation of Commitment: A Case Study of the United States Census Bureau Efforts to Implement Differential Privacy for the 2020 Decennial Census (Forthcoming in: In Proceedings of Privacy in Statistical Databases -PSD 2024)
  In 2017, the United States Census Bureau announced that because of high disclosure risk in the methodology (data swapping) used to produce tabular data for the 2010 census, a different protection mechanism based on differential privacy would be used for the 20…
  crossref Krish Muralidhar, Steven Ruggles 2024-07-31T07:49:11Z 置信度 0.70
• A Collocation-based Method for Addressing Challenges in Word-level Metric Differential Privacy
  crossref Stephen Meisenbacher, Maulik Chevli, Florian Matthes 2024-09-20T19:33:08Z 置信度 0.70
• Incompatibilities Between Current Practices in Statistical Data Analysis and Differential Privacy
  The authors discuss their experience applying differential privacy with a complex data set with the goal of enabling standard approaches to statistical data analysis. They highlight lessons learned and roadblocks encountered, distilling them into incompatibili…
  crossref Joshua Snoke, Claire McKay Bowen, Aaron R. Williams, Andrés F. Barrientos 2024-08-28T03:11:41Z 置信度 0.70
• Secure and Efficient k-anonymous Trajectory Privacy Protection Method based on Differential Privacy
  The Location-based service scheme have already involved in every aspect of People's daily life and are increasingly used in various industries. Aiming at the problem of the security and efficiency of mobile terminal users’ trajectory privacy protection in loca…
  crossref Yuanlong Fan, Cheng Song, Zhichao Wang 2024-10-15T08:39:20Z 置信度 0.70
• Consumer Data Without Compromise: Integrating Differential Privacy and GANs for Privacy-Preserving Digital Marketing
  In the rapidly evolving digital marketing landscape, the utilization of consumer data is essential for efficient targeting and personalization of marketing practices. However, the growing concerns regarding user privacy and stringent data protection regulation…
  crossref Zhuo Chen 2024-01-03T22:46:23Z 置信度 0.70
• A Privacy-Preserving Decentralized Federated Learning Framework Based on Differential Privacy
  crossref Lin Wang, Di Zhang, Min Guo, Xun Shao 2025-03-24T17:52:22Z 置信度 0.70
• Unbiased Statistical Estimation and Valid Confidence Intervals Under Differential Privacy
  crossref Christian Covington, Xi He, James Honaker, Gautam Kamath 2024-01-08T20:54:01Z 置信度 0.70