Relevant bibliographies by topics / Privacy-preserving AI

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Privacy-preserving AI'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Privacy-preserving AI"

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Vardhan, RN Shashi. "Federated Learning for Privacy-Preserving AI." International Journal of Research Publication and Reviews 6, no. 3 (2025): 9926–28. https://doi.org/10.55248/gengpi.6.0325.1321.

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Perino, Diego, Kleomenis Katevas, Andra Lutu, Eduard Marin, and Nicolas Kourtellis. "Privacy-preserving AI for future networks." Communications of the ACM 65, no. 4 (2022): 52–53. http://dx.doi.org/10.1145/3512343.

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Cheng, Yong, Yang Liu, Tianjian Chen, and Qiang Yang. "Federated learning for privacy-preserving AI." Communications of the ACM 63, no. 12 (2020): 33–36. http://dx.doi.org/10.1145/3387107.

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Diego, Perino, Katevas Kleomenis, Lutu Andra, Marin Eduard, and Kourtellis Nicolas. "Privacy-preserving ai for future networks." Communications of the ACM, April 2022 Vol. 65 (December 19, 2022): Pages 52–53. https://doi.org/10.1145/3512343.

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Telco networks and systems evolved over the years to deal with novel services. Today, they are highly complex, distributed ecosystems composed of very diverse sub-environments. They include myriad types of devices, connectivity means, protocols, and infrastructures often managed by different teams with varying expertise and tools, or even different companies.
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Pushkar, Mehendale. "Privacy-Preserving AI Through Federated Learning." Journal of Scientific and Engineering Research 8, no. 3 (2021): 249–54. https://doi.org/10.5281/zenodo.12787499.

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Federated Learning (FL) is revolutionizing the landscape of decentralized machine learning by enabling collaborative model training across multiple devices without the need to centralize data. This paper provides a comprehensive exploration of federated learning as a privacy-preserving technique in artificial intelligence (AI), examining critical challenges such as data security, communication efficiency, and inference attacks. This paper focuses on robust solutions including differential privacy, homomorphic encryption, and federated optimization to enhance the effectiveness of FL. Potential
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Wu, Shukai, Zheng Huang, Caihua Zhang, Conghe Wang, and Hongwei Chen. "Privacy-preserving face recognition with a mask-encoded microlens array." Advanced Imaging 2, no. 1 (2025): 011001. https://doi.org/10.3788/ai.2025.10018.

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Naga Sai Kiran, Venkata. "Privacy-Preserving AI at the Edge: Techniques and Applications." International Journal of Science and Research (IJSR) 10, no. 3 (2021): 2013–23. http://dx.doi.org/10.21275/sr24806050324.

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Dr. Supriya Shree, Miss. Riddhi Arya, and Mr. Saket Kumar Roy. "Enhancing Privacy Preserving Federated Learning Using Differential Privacy." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 04 (2025): 2016–27. https://doi.org/10.47392/irjaeh.2025.0294.

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Artificial Intelligence (AI) and Machine Learning (ML) play a crucial role in credit risk assessment but pose significant data privacy risks due to centralized data storage. Traditional ML models require financial institutions to share sensitive customer data, raising concerns about security breaches and regulatory compliance. Federated Learning (FL) offers a privacy-preserving alternative by enabling collaborative model training without exposing raw data. Additionally, Differential Privacy (DP) enhances FL’s security by adding mathematical noise to model updates, preventing data reconstructio
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Sreeprasad Govindankutty. "Synthetic Data Generation and Privacy-Preserving AI." International Research Journal on Advanced Engineering and Management (IRJAEM) 3, no. 05 (2025): 1681–88. https://doi.org/10.47392/irjaem.2025.0270.

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Synthetic data generation has rapidly emerged as a cornerstone technology for achieving privacy-preserving artificial intelligence (AI). In light of tightening data protection regulations and the growing ethical emphasis on safeguarding personal information, researchers have developed a range of methods to synthesize realistic datasets without compromising individual privacy. This review presents a comprehensive synthesis of existing approaches, focusing on generative adversarial networks (GANs), variational autoencoders (VAEs), and Bayesian techniques. We systematically evaluate these models
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Chananagari Prabhakar, Rohit Reddy. "Privacy-Preserving AI Database Systems in Education Analytics." International Journal of Multidisciplinary Research and Growth Evaluation 5, no. 6 (2024): 1626–29. https://doi.org/10.54660/.ijmrge.2024.5.6.1626-1629.

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The application of Artificial Intelligence (AI) in educational analytics has ushered in unprecedented enhancement in student learning prediction, learning at scale, auto-grading, and institution-level decision-making. However, the increased generation and processing of student information precipitate unprecedented concerns in privacy and security, spanning breaches and inference attacks through adversarial manipulations, unauthorized third-party information extraction, and AI model explainability restrictions. In this article, we provide a critical overview of privacy-preserving AI-based educa
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Dissertations / Theses on the topic "Privacy-preserving AI"

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Langelaar, Johannes, and Mattsson Adam Strömme. "Federated Neural Collaborative Filtering for privacy-preserving recommender systems." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-446913.

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In this thesis a number of models for recommender systems are explored, all using collaborative filtering to produce their recommendations. Extra focus is put on two models: Matrix Factorization, which is a linear model and Multi-Layer Perceptron, which is a non-linear model. With an additional purpose of training the models without collecting any sensitive data from the users, both models were implemented with a learning technique that does not require the server's knowledge of the users' data, called federated learning. The federated version of Matrix Factorization is already well-researched
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PANFILO, DANIELE. "Generating Privacy-Compliant, Utility-Preserving Synthetic Tabular and Relational Datasets Through Deep Learning." Doctoral thesis, Università degli Studi di Trieste, 2022. http://hdl.handle.net/11368/3030920.

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Due tendenze hanno rapidamente ridefinito il panorama dell'intelligenza artificiale (IA) negli ultimi decenni. La prima è il rapido sviluppo tecnologico che rende possibile un'intelligenza artificiale sempre più sofisticata. Dal punto di vista dell'hardware, ciò include una maggiore potenza di calcolo ed una sempre crescente efficienza di archiviazione dei dati. Da un punto di vista concettuale e algoritmico, campi come l'apprendimento automatico hanno subito un'impennata e le sinergie tra l'IA e le altre discipline hanno portato a sviluppi considerevoli. La seconda tendenza è la crescente co
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Kaplan, Caelin. "Compromis inhérents à l'apprentissage automatique préservant la confidentialité." Electronic Thesis or Diss., Université Côte d'Azur, 2024. http://www.theses.fr/2024COAZ4045.

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À mesure que les modèles d'apprentissage automatique (ML) sont de plus en plus intégrés dans un large éventail d'applications, il devient plus important que jamais de garantir la confidentialité des données des individus. Cependant, les techniques actuelles entraînent souvent une perte d'utilité et peuvent affecter des facteurs comme l'équité et l'interprétabilité. Cette thèse vise à approfondir la compréhension des compromis dans trois techniques de ML respectueuses de la vie privée : la confidentialité différentielle, les défenses empiriques, et l'apprentissage fédéré, et à proposer des méth
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Amaral, João Miguel Vaz Tello da Gama. "Scalable and privacy preserving AI in federated environments." Master's thesis, 2021. https://hdl.handle.net/10216/137218.

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Books on the topic "Privacy-preserving AI"

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Shenoy, Jayavanth, and Patrick Grinaway. High Performance Privacy Preserving AI. Now Publishers, 2024.

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Lilhore, Simaiya, and Poongodi. Federated Learning and Privacy-Preserving in Healthcare AI. IGI Global, 2024.

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Lilhore, Simaiya, and Poongodi. Federated Learning and Privacy-Preserving in Healthcare AI. IGI Global, 2024.

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Lilhore, Simaiya, and Poongodi. Federated Learning and Privacy-Preserving in Healthcare AI. IGI Global, 2024.

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Lilhore, Simaiya, and Poongodi. Federated Learning and Privacy-Preserving in Healthcare AI. IGI Global, 2024.

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Privacy-Preserving Computing: For Big Data Analytics and AI. Cambridge University Press, 2023.

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Privacy-Preserving Computing: For Big Data Analytics and AI. Cambridge University Press, 2023.

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Book chapters on the topic "Privacy-preserving AI"

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Palle, Ranadeep Reddy, and Krishna Chaitanya Rao Kathala. "Privacy-Preserving AI Techniques." In Privacy in the Age of Innovation. Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0461-8_5.

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Al-Kuwari, Saif. "Privacy-Preserving AI in Healthcare." In Multiple Perspectives on Artificial Intelligence in Healthcare. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67303-1_6.

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Timan, Tjerk, and Zoltan Mann. "Data Protection in the Era of Artificial Intelligence: Trends, Existing Solutions and Recommendations for Privacy-Preserving Technologies." In The Elements of Big Data Value. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68176-0_7.

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AbstractThis chapter addresses privacy challenges that stem particularly from working with big data. Several classification schemes of such challenges are discussed. The chapter continues by classifying the technological solutions as proposed by current state-of-the-art research projects. Three trends are distinguished: (1) putting the end user of data services back as the central focal point of Privacy-Preserving Technologies, (2) the digitisation and automation of privacy policies in and for big data services and (3) developing secure methods of multi-party computation and analytics, allowin
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Long, Guodong, Tao Shen, Yue Tan, Leah Gerrard, Allison Clarke, and Jing Jiang. "Federated Learning for Privacy-Preserving Open Innovation Future on Digital Health." In Humanity Driven AI. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72188-6_6.

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Iyengar, S. S., Seyedsina Nabavirazavi, Yashas Hariprasad, Prasad HB, and C. Krishna Mohan. "Privacy-Preserving AI (Federated Learning) for Digital Forensics." In Signals and Communication Technology. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-89327-8_5.

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Bazzotti, Gabriele, Alfonso Emilio Gerevini, Nir Lipovetzky, Francesco Percassi, Alessandro Saetti, and Ivan Serina. "Iterative Width Search for Multi Agent Privacy-Preserving Planning." In AI*IA 2018 – Advances in Artificial Intelligence. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03840-3_32.

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Müftüoğlu, Z., M. A. Kızrak, and T. Yıldırım. "Privacy-Preserving Mechanisms with Explainability in Assistive AI Technologies." In Learning and Analytics in Intelligent Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87132-1_13.

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Ammirata, Germano, Gennaro Junior Pezzullo, Salvatore Contino, Beniamino Di Martino, and Roberto Pirrone. "Federated Learning Framework for Privacy-Preserving AI in Healthcare." In Lecture Notes on Data Engineering and Communications Technologies. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-87778-0_31.

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Bertl, Markus, Yngve Lamo, Martin Leucker, et al. "Challenges for AI in Healthcare Systems." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-73741-1_11.

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AbstractThis paper overviews the challenges of using artificial intelligence (AI) methods when building healthcare systems, as discussed at the AIsola Conference in 2023. It focuses on the topics (i) medical data, (ii) decision support, (iii) software engineering for AI-based health systems, (iv) regulatory affairs as well as (v) privacy-preserving machine learning and highlights the importance and challenges involved when utilizing AI in healthcare systems.
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Rao, Kartikey, Ananya Gupta, Praveen Arora, and Suman Madan. "Privacy-Preserving AI: A Comprehensive Approach to Big Data Security." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-6106-7_37.

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Conference papers on the topic "Privacy-preserving AI"

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Gao, David, Ian Miller, Ali Allami, and Dan Lin. "Preserving Privacy During Reinforcement Learning With AI Feedback." In 2024 IEEE 6th International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications (TPS-ISA). IEEE, 2024. https://doi.org/10.1109/tps-isa62245.2024.00033.

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Dandu, Hemanth. "Federated Learning for Privacy-Preserving AI in Healthcare." In 2025 3rd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS). IEEE, 2025. https://doi.org/10.1109/icssas66150.2025.11081017.

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Mehta, Shiva, and Aseem Aneja. "Privacy-Preserving AI: Leveraging Federated Reinforcement Learning in Distributed Systems." In 2024 International Conference on Electrical Electronics and Computing Technologies (ICEECT). IEEE, 2024. http://dx.doi.org/10.1109/iceect61758.2024.10739054.

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Sawyer, Scott M. "Privacy-Preserving AI for Document Understanding with Controlled Unclassified Information." In 2024 IEEE High Performance Extreme Computing Conference (HPEC). IEEE, 2024. https://doi.org/10.1109/hpec62836.2024.10938417.

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Tabet, Salam, Ayman Kayssi, and Imad H. Elhajj. "AI-Enhanced Mobile Diminished Reality for Preserving 3D Visual Privacy." In 2024 2nd International Conference on Intelligent Metaverse Technologies & Applications (iMETA). IEEE, 2024. https://doi.org/10.1109/imeta62882.2024.10807959.

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Sharma, Nandini, and Pritaj Yadav. "Privacy-Preserving Techniques in AI-Based Healthcare: A Comprehensive Review." In 2024 IEEE 11th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON). IEEE, 2024. https://doi.org/10.1109/upcon62832.2024.10983387.

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V, Dilli Ganesh, R. M. Bommi, and Nandhini T J. "Enhancing Healthcare Data Security and Privacy through AI-Driven Encryption and Privacy-Preserving Techniques." In 2025 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI). IEEE, 2025. https://doi.org/10.1109/icdsaai65575.2025.11011747.

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Amin, Al, Kamrul Hasan, Sharif Ullah, and Liang Hong. "AI-Driven Secure Data Sharing: A Trustworthy and Privacy-Preserving Approach." In 2025 International Conference on Computing, Networking and Communications (ICNC). IEEE, 2025. https://doi.org/10.1109/icnc64010.2025.10993885.

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Ghader, Mohammadnavid, Saeed Reza Kheradpisheh, Bahar Farahani, and Mahmood Fazlali. "Enabling Privacy-Preserving Edge AI: Federated Learning Enhanced with Forward-Forward Algorithm." In 2024 IEEE International Conference on Omni-layer Intelligent Systems (COINS). IEEE, 2024. http://dx.doi.org/10.1109/coins61597.2024.10622150.

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Vadisetty, Rahul, and Anand Polamarasetti. "AI-Generated Privacy-Preserving Protocols for Cross-Cloud Data Sharing and Collaboration." In 2024 IEEE 4th International Conference on ICT in Business Industry & Government (ICTBIG). IEEE, 2024. https://doi.org/10.1109/ictbig64922.2024.10911392.

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Reports on the topic "Privacy-preserving AI"

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Pasupuleti, Murali Krishna. Next-Generation Extended Reality (XR): A Unified Framework for Integrating AR, VR, and AI-driven Immersive Technologies. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv325.

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Abstract: Extended Reality (XR), encompassing Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR), is evolving into a transformative technology with applications in healthcare, education, industrial training, smart cities, and entertainment. This research presents a unified framework integrating AI-driven XR technologies with computer vision, deep learning, cloud computing, and 5G connectivity to enhance immersion, interactivity, and scalability. AI-powered neural rendering, real-time physics simulation, spatial computing, and gesture recognition enable more realistic and adap
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Pasupuleti, Murali Krishna. Augmented Human Intelligence: Converging Generative AI, Quantum Computing, and XR for Enhanced Human-Machine Synergy. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv525.

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Abstract: Augmented Human Intelligence (AHI) represents a paradigm shift in human-AI collaboration, leveraging Generative AI, Quantum Computing, and Extended Reality (XR) to enhance cognitive capabilities, decision-making, and immersive interactions. Generative AI enables real-time knowledge augmentation, automated creativity, and adaptive learning, while Quantum Computing accelerates AI optimization, pattern recognition, and complex problem-solving. XR technologies provide intuitive, immersive environments for AI-driven collaboration, bridging the gap between digital and physical experiences.
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Pasupuleti, Murali Krishna. Automated Smart Contracts: AI-powered Blockchain Technologies for Secure and Intelligent Decentralized Governance. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv425.

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Abstract: Automated smart contracts represent a paradigm shift in decentralized governance by integrating artificial intelligence (AI) with blockchain technologies to enhance security, scalability, and adaptability. Traditional smart contracts, while enabling trustless and automated transactions, often lack the flexibility to adapt to dynamic regulatory frameworks, evolving economic conditions, and real-time security threats. AI-powered smart contracts leverage machine learning, reinforcement learning, and predictive analytics to optimize contract execution, detect fraudulent transactions, and
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Journal articles
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