Suchergebnisse - "Privacy-Preserving Techniques for Data Analysis and Machine Learning"

Collection and Analysis of Sensitive Data with Privacy Protection by a Distributed Randomized Response Protocol

Autoren: Faisal Imran Rosa Meo

Quelle: Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing. :1415-1424

Schlagwörter: Statistics and Probability, Alternative medicine, Information privacy, FOS: Health sciences, Diagnostic Methods for COVID-19 Detection, Estimator, 7. Clean energy, Artificial Intelligence, Computer security, Health Sciences, FOS: Mathematics, Pathology, Privacy protection, Randomized Response, Local Differential Privacy, Contingency Tables, Distributed computation protocol, Differential Privacy, Statistical Methods for Sensitive Survey Questions, Privacy Preservation, Computer network, Randomized Response Technique, Statistics, Privacy-Preserving Techniques for Data Analysis and Machine Learning, 16. Peace & justice, Computer science, Infectious Diseases, Computer Science, Physical Sciences, Protocol (science), Sensitive Questions, Medicine, Stratified Sampling, Randomized response, Mathematics

Dateibeschreibung: application/pdf

Perfectly Accurate Membership Inference by a Dishonest Central Server in Federated Learning

Autoren: Georg Pichler Marco Romanelli Leonardo Rey Vega et al.

Quelle: IEEE Transactions on Dependable and Secure Computing. 21:4290-4296

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial intelligence, Computer Science - Cryptography and Security, Computer Science - Artificial Intelligence, Set (abstract data type), Evolutionary biology, 02 engineering and technology, MNIST database, Epistemology, Contrast (vision), LEARNING, Adversarial Robustness in Deep Learning Models, Machine Learning (cs.LG), Deep Learning, Inference, Artificial Intelligence, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, FEDERATED, Data mining, Biology, NEURAL NET, Membership Inference Attacks, Chromatography, Sample (material), Privacy-Preserving Techniques for Data Analysis and Machine Learning, Deep learning, Computer science, FOS: Philosophy, ethics and religion, Programming language, Philosophy, Chemistry, Artificial Intelligence (cs.AI), Function (biology), Computer Science, Physical Sciences, Simple (philosophy), MEMBERSHIP INFERENCE, Cryptography and Security (cs.CR), Federated Learning, Training set

Dateibeschreibung: application/pdf

Zugangs-URL: http://arxiv.org/abs/2203.16463
http://hdl.handle.net/11336/255711

From Barriers to Boosters: Optimizing Open Government Data through Publishing Guidelines and Data Protection Strategies

Autoren: Khadidja Bouchelouche Abdessamedréda Ghomari Leila Zemmouchi-Ghomari

Quelle: Recent Research Reviews Journal. 3:56-69

Schlagwörter: Data Quality Assessment and Improvement, Metric (unit), FOS: Political science, Social Sciences, FOS: Law, Management Science and Operations Research, Data management, Decision Sciences, Data science, Database, FOS: Economics and business, Open government, Artificial Intelligence, Evolution and Impact of E-Government Initiatives, Open Data, Data publishing, 11. Sustainability, Government (linguistics), Business, Political science, Semantic Web, Publishing, Marketing, Discoverability, Linked data, Open data, Privacy-Preserving Techniques for Data Analysis and Machine Learning, Data quality, 1. No poverty, Linguistics, 15. Life on land, Data governance, 16. Peace & justice, Computer science, FOS: Philosophy, ethics and religion, World Wide Web, Philosophy, 13. Climate action, Computer Science, Physical Sciences, Political Science and International Relations, FOS: Languages and literature, Law

Handling of Personal Data by Smart Home Equipment: an Exploratory Analysis in the Context of LGPD

Autoren: Thiago Adriano Coleti Renato Balancieri André Menolli et al.

Quelle: Journal on Interactive Systems, Vol 15, Iss 1 (2024)

Schlagwörter: Smart Home, Computer engineering. Computer hardware, Sociology and Political Science, Social Sciences, 02 engineering and technology, 7. Clean energy, Privacy and Security, Data science, TK7885-7895, QA76.75-76.765, Context (archaeology), Exploratory data analysis, Artificial Intelligence, Personal Data, 11. Sustainability, 0202 electrical engineering, electronic engineering, information engineering, Computer software, Data mining, 2. Zero hunger, Geography, 05 social sciences, Privacy-Preserving Techniques for Data Analysis and Machine Learning, 1. No poverty, 16. Peace & justice, Computer science, Exploratory analysis, Archaeology, Computer Science, Physical Sciences, 0503 education, Internet Users' Information Privacy Concerns, General Data Protection Regulation

Zugangs-URL: https://doaj.org/article/e26728363be642bb8b95cf7a660e598c

SHAPER: A General Architecture for Privacy-Preserving Primitives in Secure Machine Learning

Autoren: Ziyuan Liang Qin Jin Zhiyong Wang et al.

Quelle: Transactions on Cryptographic Hardware and Embedded Systems, Vol 2024, Iss 2 (2024)

Schlagwörter: FOS: Computer and information sciences, Secure Multi-party Computation, Computer engineering. Computer hardware, Artificial intelligence, Information technology, Privacy-Preserving Machine Learning, Security Challenges in Cloud Computing, 7. Clean energy, Searchable Encryption, Visual arts, TK7885-7895, Artificial Intelligence, Computer security, Architecture, Secure Computation, Human–computer interaction, Advanced Cryptographic Schemes and Protocols, Multi-Party Computation, Privacy-Preserving Techniques for Data Analysis and Machine Learning, Additive Homomorphic Encryption, Hardware Accelerator, T58.5-58.64, Computer science, Computer Science, Physical Sciences, Privacy-Preserving Computation, Homomorphic Encryption, FOS: Civil engineering, Art, Information Systems

Zugangs-URL: https://doaj.org/article/1a0d06d407194293903d7dae54231b8c

Federated learning enables 6 G communication technology: Requirements, applications, and integrated with intelligence framework

Autoren: Mohammad Kamrul Hasan Arslan Habib Shayla Islam et al.

Quelle: Alexandria Engineering Journal, Vol 91, Iss, Pp 658-668 (2024)

Schlagwörter: Communication technology, Advanced Cryptographic Schemes and Protocols, Integrated learning, Knowledge management, Privacy-Preserving Techniques for Data Analysis and Machine Learning, Engineering (General). Civil engineering (General), Intelligent Reflecting Surfaces in Wireless Communications, Computer science, 7. Clean energy, 6G Networks, Systems engineering, Federate Learning, Engineering, Artificial Intelligence, Electrical engineering, Machine learning, Computer Science, Physical Sciences, FOS: Electrical engineering, electronic engineering, information engineering, TA1-2040, Electrical and Electronic Engineering, Electronics, Federated Learning, 6G

Zugangs-URL: https://doaj.org/article/43c8b33408f249f985400c9cb408a2e0

FedDCT: Federated Learning of Large Convolutional Neural Networks on Resource-Constrained Devices Using Divide and Collaborative Training

Autoren: Quan Dong Nguyen Hieu H. Pham Kok‐Seng Wong et al.

Quelle: IEEE Transactions on Network and Service Management. 21:418-436

Schlagwörter: Artificial neural network, FOS: Computer and information sciences, Artificial intelligence, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, Convolutional neural network, 02 engineering and technology, Edge device, Bottleneck, Deep Learning, Artificial Intelligence, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, Cloud computing, Optimization Methods in Machine Learning, Embedded system, Privacy-Preserving Techniques for Data Analysis and Machine Learning, Deep learning, Computer science, Distributed computing, Process (computing), Overhead (engineering), Operating system, Computer Science, Physical Sciences, Federated Learning

Zugangs-URL: http://arxiv.org/abs/2211.10948

A Probabilistic Approach for Missing Data Imputation

Autoren: Muhammed Nazmul Arefin Abdul Kadar Muhammad Masum

Quelle: Complexity, Vol 2024 (2024)

Schlagwörter: Statistics and Probability, Artificial intelligence, Missing data, 0206 medical engineering, Privacy-Preserving Techniques for Data Analysis and Machine Learning, Multiple Imputation, QA75.5-76.95, 02 engineering and technology, Regularization and Variable Selection Methods, Computer science, Big data, Methods for Handling Missing Data in Statistical Analysis, Artificial Intelligence, Electronic computers. Computer science, Computer Science, Physical Sciences, Machine learning, 8. Economic growth, 0202 electrical engineering, electronic engineering, information engineering, FOS: Mathematics, Imputation (statistics), Data mining, Mathematics, Probabilistic logic

Dateibeschreibung: text/xhtml

Zugangs-URL: https://doaj.org/article/6dca6583d7f840dc9ac8aed70ac23714

An Improved and Efficient Coercion-resistant Measure for Electronic Voting System

Autoren: Tzer‐Long Chen Chia‐Hui Liu Ya-Hui Ou et al.

Quelle: International Journal of Information Security. 23:2637-2654

Schlagwörter: Secure Multi-party Computation, FOS: Political science, FOS: Law, 02 engineering and technology, 7. Clean energy, Artificial Intelligence, Computer security, Bullet voting, 0202 electrical engineering, electronic engineering, information engineering, Business, Coercion (linguistics), Political science, Order (exchange), Secure Computation, Advanced Cryptographic Schemes and Protocols, Electronic voting, Politics, Privacy-Preserving Techniques for Data Analysis and Machine Learning, Linguistics, Volition (linguistics), 16. Peace & justice, Computer science, Cardinal voting systems, FOS: Philosophy, ethics and religion, Philosophy, Machine Learning for Internet Traffic Classification, E-Voting, Computer Science, Physical Sciences, Cryptography, FOS: Languages and literature, Internet privacy, Voting, Law, Finance

Fortifying Federated Learning in IIoT: Leveraging Blockchain and Digital Twin Innovations for Enhanced Security and Resilience

Autoren: Prathiba, SB Govindarajan, Y Pranav Amirtha Ganesan, V et al.

Quelle: IEEE Access, Vol 12, Pp 68968-68980 (2024)

Schlagwörter: FOS: Computer and information sciences, 7. Clean energy, 09 Engineering, data poisoning, Blockchain, Artificial Intelligence, Computer security, 10 Technology, digital twin, Secure Computation, 9. Industry and infrastructure, Physics, 4. Education, Privacy-Preserving Techniques for Data Analysis and Machine Learning, Blockchain and Internet of Things Integration, decentralized federated learning, 16. Peace & justice, Computer science, TK1-9971, Resilience (materials science), Computer Science, Physical Sciences, Thermodynamics, model poisoning, 08 Information and Computing Sciences, Electrical engineering. Electronics. Nuclear engineering, industrial internet of things, Federated Learning, Information Systems

Zugangs-URL: https://doaj.org/article/402846f923ec4a1799b5ab59ab5eafe2
https://e-space.mmu.ac.uk/635089/

MAPLE: MArkov Process Leakage attacks on Encrypted Search

Autoren: Seny Kamara Abdelkarim Kati Tarik Moataz et al.

Quelle: Proceedings on Privacy Enhancing Technologies. 2024:430-446

Schlagwörter: Economics, Markov chain, Macroeconomics, Encryption, 02 engineering and technology, Leverage (statistics), Searchable Encryption, Theoretical computer science, Artificial Intelligence, Computer security, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, Attribute-Based Encryption, Data mining, Secure Computation, Advanced Cryptographic Schemes and Protocols, Privacy-Preserving Techniques for Data Analysis and Machine Learning, Computer science, Information leakage, Leakage (economics), Machine Learning for Internet Traffic Classification, Computer Science, Physical Sciences, Hash function, Cryptography, Encrypted Traffic, Homomorphic Encryption

Privacy Preserving Inference for Deep Neural Networks: Optimizing Homomorphic Encryption for Efficient and Secure Classification

Autoren: Aftab Akram Fawad Khan Shahzaib Tahir et al.

Quelle: IEEE Access, Vol 12, Pp 15684-15695 (2024)

Schlagwörter: Artificial neural network, Artificial intelligence, Information privacy, Combinatorial Optimization and Complexity Theory, homomorphic encryption, Encryption, Convolutional neural network, cloud server, MNIST database, Inference, Artificial Intelligence, Computer security, Machine learning, Cloud computing, activation function, Differential Privacy, Data mining, Secure Computation, Privacy Preservation, Advanced Cryptographic Schemes and Protocols, Privacy-Preserving Techniques for Data Analysis and Machine Learning, security and privacy, approximation techniques, 16. Peace & justice, Computer science, Homomorphic encryption, TK1-9971, Operating system, Computational Theory and Mathematics, Computer Science, Physical Sciences, Electrical engineering. Electronics. Nuclear engineering, Privacy-Preserving Computation, Homomorphic Encryption

Zugangs-URL: https://doaj.org/article/503e72ca482f4492baa13e4ac604e156

Exploring the potential of federated learning in mental health research: a systematic literature review: a systematic literature review

Autoren: Samar Samir Khalil Noha S. Tawfik Marco Spruit

Quelle: Applied Intelligence. 54:1619-1636

Schlagwörter: Pooling, Artificial intelligence, Economics, Federated learning, Health Informatics, Mathematical analysis, 7. Clean energy, Data science, Artificial Intelligence, Computer security, Health Sciences, Machine learning, FOS: Mathematics, Pathology, Raw data, 10. No inequality, Economic growth, Artificial Intelligence in Medicine, Psychiatry, Domain (mathematical analysis), Human–computer interaction, Deep Learning Applications in Healthcare, Privacy-Preserving Techniques for Data Analysis and Machine Learning, Medical diagnosis, Health care, Medical Concept Embedding, Computer science, Programming language, 3. Good health, Mental illness, Computer Science, Physical Sciences, Medicine, Mental health, Federated Learning, Mathematics

Dateibeschreibung: application/pdf

Zugangs-URL: https://hdl.handle.net/1887/4197237
https://hdl.handle.net/1887/4038333

Quantum-empowered federated learning and 6G wireless networks for IoT security: Concept, challenges and future directions

Autoren: Danish Javeed Muhammad Shahid Saeed Ijaz Ahmad et al.

Quelle: Future Generation Computer Systems. 160:577-597

Schlagwörter: Computer network, Computer Networks and Communications, Physics, Internet of Things, Privacy-Preserving Techniques for Data Analysis and Machine Learning, Computer science, Quantum mechanics, 7. Clean energy, Quantum, IoT Devices, Cooperative Diversity in Wireless Networks, Artificial Intelligence, Machine Learning for Internet Traffic Classification, Computer security, Computer Science, Physical Sciences, Wireless, Telecommunications, Federated Learning, Wireless network, Secure Computation

A novel privacy-aware global infrastructure for ecological footprint calculator based on the Internet of things and blockchain

Autoren: Nadim Elsakaan Kamal Amroun

Quelle: The Journal of Supercomputing. 80:9494-9531

Schlagwörter: FOS: Computer and information sciences, Internet of Things, Immutability, 7. Clean energy, 12. Responsible consumption, Blockchain, Server, Artificial Intelligence, Computer security, 11. Sustainability, Ecological footprint, Biology, 2. Zero hunger, Ecology, 9. Industry and infrastructure, Blockchain and Internet of Things Integration, Privacy-Preserving Techniques for Data Analysis and Machine Learning, 16. Peace & justice, Computer science, World Wide Web, Sustainability, 13. Climate action, FOS: Biological sciences, Computer Science, Physical Sciences, 8. Economic growth, The Internet, Information Systems

Self-Organizing Democratized Learning: Toward Large-Scale Distributed Learning Systems

Autoren: Minh N. H. Nguyen Shashi Raj Pandey Tri Nguyen Dang et al.

Quelle: Nguyen, M N H, Pandey, S R, Dang, T N, Huh, E-N, Tran, N H, Saad, W & Hong, C S 2023, 'Self-Organizing Democratized Learning : Toward Large-Scale Distributed Learning Systems', IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 12, pp. 10698-10710. https://doi.org/10.1109/TNNLS.2022.3170872
Nguyen, M N H, Pandey, S R, Dang, T N, Huh, E-N, Tran, N H, Saad, W & Hong, C S 2023, ' Self-Organizing Democratized Learning : Toward Large-Scale Distributed Learning Systems ', IEEE Transactions on Neural Networks and Learning Systems . https://doi.org/10.1109/TNNLS.2022.3170872

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial intelligence, Generalization, FOS: Mechanical engineering, Machine Learning (stat.ML), MNIST database, Participatory Sensing, Unsupervised learning, Mathematical analysis, Machine Learning (cs.LG), Engineering, Theoretical computer science, Philosophical considerations, Statistics - Machine Learning, Artificial Intelligence, Self-Reconfigurable Robotic Systems and Modular Robotics, Machine learning, FOS: Mathematics, distributed artificial intelligences (AIs), Distance learning, Learning systems, 9. Industry and infrastructure, Mechanical Engineering, Privacy-Preserving Techniques for Data Analysis and Machine Learning, Distributed Control, Computational modeling, Democratized learning, Deep learning, Crowdsourcing for Research and Data Collection, 16. Peace & justice, self-organization, Computer science, Computer Science Applications, Computer aided instruction, Task analysis, Computer Science, Physical Sciences, hierarchical learning, Federated Learning, Mathematics

Dateibeschreibung: application/pdf

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/35536803
http://arxiv.org/abs/2007.03278
http://www.scopus.com/inward/record.url?scp=85132540104&partnerID=8YFLogxK
https://vbn.aau.dk/ws/files/698876079/Self-Organizing_Democratized_Learning_Toward_Large-Scale_Distributed_Learning_Systems.pdf
https://doi.org/10.1109/TNNLS.2022.3170872
https://vbn.aau.dk/da/publications/fd0271f3-0474-4ce5-86ec-fcf7348a90ef
https://vbn.aau.dk/da/publications/312a1b6d-ee50-4cfe-9952-e863be7b19c9
https://vbn.aau.dk/en/publications/fd0271f3-0474-4ce5-86ec-fcf7348a90ef
https://doi.org/10.1109/TNNLS.2022.3170872
http://hdl.handle.net/10919/110774
https://doi.org/10.1109/TNNLS.2022.3170872

A Privacy-Preserving Federated Learning Framework for Blockchain Networks

Autoren: Youssif Abuzied Mohamed Ghanem Fadi Dawoud et al.

Quelle: Cluster Computing. 27:3997-4014

Schlagwörter: FOS: Computer and information sciences, Computer network, Advanced Cryptographic Schemes and Protocols, 9. Industry and infrastructure, 4. Education, Privacy-Preserving Techniques for Data Analysis and Machine Learning, Blockchain and Internet of Things Integration, Federated learning, Lattice-based Cryptography, 02 engineering and technology, Computer science, 7. Clean energy, Searchable Encryption, Distributed computing, Blockchain, Artificial Intelligence, Computer security, Computer Science, Physical Sciences, 0202 electrical engineering, electronic engineering, information engineering, Privacy-Preserving Computation, 10. No inequality, Federated Learning, Information Systems

5 G and beyond wireless network optimization through RIS technology: a pricing game

Autoren: Lhoussaine Daoudi Mohamed Baslam Mohamed Zagour et al.

Quelle: Cluster Computing. 27:5867-5877

Schlagwörter: Artificial intelligence, Economics, FOS: Political science, FOS: Law, Operations research, Cournot competition, Rationality, 7. Clean energy, Nash equilibrium, 12. Responsible consumption, Engineering, Artificial Intelligence, Machine learning, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Mathematics, Base station, Reconfigurable Intelligent Surfaces, Electrical and Electronic Engineering, Stability (learning theory), Political science, Game theory, Smart Radio Environments, 2. Zero hunger, Computer network, Mathematical economics, 9. Industry and infrastructure, Bounded rationality, Mathematical optimization, Privacy-Preserving Techniques for Data Analysis and Machine Learning, 1. No poverty, Intelligent Reflecting Surfaces in Wireless Communications, Computer science, Duopoly, Physical Sciences, Computer Science, 8. Economic growth, Wireless, Telecommunications, Law, Mathematics

Advancing Pandemic Preparedness in Healthcare 5.0: A Survey of Federated Learning Applications

Autoren: Saeed Hamood Alsamhi Ammar Hawbani Alexey V. Shvetsov et al.

Quelle: Advances in Human-Computer Interaction, Vol 2023 (2023)

Schlagwörter: FOS: Computer and information sciences, Radiology, Nuclear Medicine and Imaging, Preparedness, Knowledge management, FOS: Political science, Transformative learning, FOS: Law, 02 engineering and technology, Infectious disease (medical specialty), Role of Digital Technologies in Pandemic Contact Tracing, 03 medical and health sciences, 0302 clinical medicine, Context (archaeology), Artificial Intelligence, Health Sciences, 0202 electrical engineering, electronic engineering, information engineering, Pathology, Psychology, Business, Disease, Political science, Biology, Pandemic, Pedagogy, Privacy-Preserving Techniques for Data Analysis and Machine Learning, Health care, Paleontology, QA75.5-76.95, Applications of Deep Learning in Medical Imaging, Computer science, 3. Good health, Coronavirus disease 2019 (COVID-19), FOS: Psychology, Electronic computers. Computer science, Computer Science, Physical Sciences, Medicine, Federated Learning, Law, Information Systems

Dateibeschreibung: text/xhtml

Zugangs-URL: https://doaj.org/article/1da54570f165421cbe21725917a7e27f

Optimized federated learning based on Adagrad algorithm and algorithm optimization

Autoren: Eric King‐wah Chu Dengbo Li Yangfan Tong

Quelle: Applied and Computational Engineering. 19:9-17

Schlagwörter: FOS: Computer and information sciences, Artificial neural network, Artificial intelligence, Gradient descent, 4. Education, Mathematical optimization, Privacy-Preserving Techniques for Data Analysis and Machine Learning, Digital Image Processing and Artificial Neural Networks, Federated learning, 15. Life on land, Computer science, 7. Clean energy, Algorithm, Optimization algorithm, Artificial Intelligence, Computer Science, Physical Sciences, Machine learning, FOS: Mathematics, Data Mining in Various Applications, Federated Learning, Mathematics, Information Systems