NSTL国家科技图书文献中心

1. The Randomness Complexity of Differential Privacy NSTL国家科技图书文献中心

Clement L. Canonne | Francis E. Su ... - 《16th Innovations in Theoretical Computer Science Conference,Part 1 of 3》 - Innovations in Theoretical Computer Science Conference - 2025, - 共21页

摘要： complexity of differential* privacy, i.e., how many random* | , unbounded differential* privacy and privacy-loss parameter* | , provided the privacy-loss parameters satisfy ε, δ ≤ 1 | We initiate the study of the randomness | bits an algorithm needs in order to generate accurate

关键词： Differential privacy | Randomness | Geometry

2. Differential Privacy on Trust Graphs NSTL国家科技图书文献中心

Badih Ghazi | Ravi Kumar ... - 《16th Innovations in Theoretical Computer Science Conference,Part 2 of 3》 - Innovations in Theoretical Computer Science Conference - 2025, - 共23页

摘要：We study differential* privacy (DP) in a multi* | privacy-utility trade-off than in the well-studied local | -party setting where each party only trusts a (known | ) subset of the other parties with its data. Specifically | , given a trust graph where vertices correspond to

关键词： Differential privacy | Trust graphs | Minimum dominating set | Packing number

3. Quantum neural network classifier with differential privacy NSTL国家科技图书文献中心

Li, Guodong | Yu, Fangce ... - 《Physica Scripta》 - 2025,100(3) - 共15页

摘要： QNN model integrated with differential privacy | ensure differential* privacy. Our theoretical analysis* | demonstrates that in differential* privacy settings, the model* | privacy leakage risks that could undermine users | effectively protects privacy* while demonstrating good*

关键词： quantum computation | quantum neural network | differential privacy | data re-uploading

4. Bayesian networks based on differential privacy for financial data privacy NSTL国家科技图书文献中心

Ni Li | Jianwei Yang ... - 《Third International Conference on Electrical,Electronics,and Information Engineering (EEIE 2024)》 - International Conference on Electrical,Electronics,and Information Engineering - 2025, - 1351212.1～1351212.6 - 共6页

摘要： networks and differential* privacy, innovatively* | As the demand for privacy* protection continues* | protecting privacy. | to grow, ensuring effective financial data | processing while preventing sensitive information leakage

关键词： Finance | Bayesian network | Differential privacy

5. FUD-LDP: Fully User Driven Local Differential Privacy NSTL国家科技图书文献中心

Gnanakumar Thedchana... | Michael Bewong ... - 《Web Information Systems Engineering - WISE 2024,Part V》 - International Conference on Web Information Systems Engineering - 2025, - 74～83 - 共10页

摘要： statistical aggregate, Local Differential* Privacy (LDP) has* | differential privacy mechanism denoted as FUD-LDP which gives | become the de facto mechanism for preserving privacy | enforcing a preset privacy-level for all participants | participants with the freedom to choose privacy-levels, such

关键词： UD-LDP | User-Driven local differential privacy | Data collection | Data privacy | Differential privacy

6. Secure Two-Party Frequent Itemset Mining With Guaranteeing Differential Privacy NSTL国家科技图书文献中心

Wenjie Chen | Haoyu Chen ... - 《IEEE transactions on mobile computing》 - 2025,24(1) - 276～292 - 共17页

摘要： information. Differential* privacy has been utilized in* | to the other while achieving differential privacy | design practical methods for privacy-preserving | individuals. However, addressing how to ensure differential | privacy for two-party frequent itemset mining remains

关键词： Itemsets | Data mining | Databases | Protocols | Differential privacy | Mobile computing | Privacy

7. LDPMF: Local differential privacy enhanced matrix factorization for advanced recommendation NSTL国家科技图书文献中心

Li X. | Zhou W.... - 《Knowledge-based systems》 - 2025,309(Jan.30) - 1.1～1.15 - 共15页

摘要： is that privacy* leakage may become particularly* | easily. To cope with these challenges, differential | privacy preservation techniques have become powerful | tools to solve the privacy* leakage problem in* | issues such as the balance between privacy* protection*

关键词： Differential privacy | Matrix factorization | Recommender system | Theoretical analysis

8. Complex-Valued Federated Learning with Differential Privacy and MRI Applications NSTL国家科技图书文献中心

Anneliese Riess | Alexander Ziller ... - 《Medical Image Computing and Computer Assisted Intervention - MICCAI 2024 Workshops》 - International Conference on Medical Image Computing and Computer Assisted Intervention | International Workshop on Interpretability of Machine Intelligence in Medical Image Computing | Workshop on Distributed,Collaborative,and Federated Learning | Embodied AI and Robotics for HealTHcare | International Skin Imaging Collaboration - 2025, - 191～203 - 共13页

摘要：Federated learning enhanced with Differential | Privacy (DP) is a powerful privacy-preserving strategy | ), yielding excellent utility and privacy. Our results | with robust privacy-preserving techniques in the MRI | to protect individuals sharing their sensitive data

关键词： Federated learning | Differential privacy | Complex numbers

9. A Differential Privacy Federated Learning Approach for Diabetic Retinopathy Detection NSTL国家科技图书文献中心

Yicheng Li | Yijia Zhang ... - 《Advanced Data Mining and Applications,Part IV》 - International Conference on Advanced Data Mining and Applications - 2025, - 186～199 - 共14页

摘要： differential privacy (DP) into our self-designed federated | significantly reduce diagnostic time. Given the privacy | effectively protect patient privacy* is very popular. However* | , attackers can still exploit privacy* information through* | address privacy* concerns, we introduce a novel DR*

关键词： Diabetic retinopathy | Federated learning | Differential privacy | Privacy protection

10. Differential Privacy, Firm-Level Data, and the Binomial Pathology NSTL国家科技图书文献中心

David D. Clark | Simson Garfinkel ... - 《IEEE security & privacy》 - 2025,23(1) - 8～16 - 共9页

摘要：Differential privacy (DP) is a privacy | -enhancing technology that is increasingly being used by | policy makers in the United States and Europe, but it | is not well suited to protect corporate proprietary | information that is used to produce some kinds of aggregate

关键词： Data privacy | Databases | Protection | Data models | Computer hacking | Differential privacy | Pathology | Identification of persons | Information security | Data security...

摘要： complexity of differential privacy, i.e., how many random | , unbounded differential privacy and privacy-loss parameter | , provided the privacy-loss parameters satisfy ε, δ ≤ 1 | We initiate the study of the randomness | bits an algorithm needs in order to generate accurate

关键词： Differential privacy | Randomness | Geometry

摘要：We study differential privacy (DP) in a multi | privacy-utility trade-off than in the well-studied local | -party setting where each party only trusts a (known | ) subset of the other parties with its data. Specifically | , given a trust graph where vertices correspond to

关键词： Differential privacy | Trust graphs | Minimum dominating set | Packing number

摘要： QNN model integrated with differential privacy | ensure differential privacy. Our theoretical analysis | demonstrates that in differential privacy settings, the model | privacy leakage risks that could undermine users | effectively protects privacy while demonstrating good

关键词： quantum computation | quantum neural network | differential privacy | data re-uploading

摘要： networks and differential privacy, innovatively | As the demand for privacy protection continues | protecting privacy. | to grow, ensuring effective financial data | processing while preventing sensitive information leakage

关键词： Finance | Bayesian network | Differential privacy

关键词： UD-LDP | User-Driven local differential privacy | Data collection | Data privacy | Differential privacy

摘要： information. Differential privacy has been utilized in | to the other while achieving differential privacy | design practical methods for privacy-preserving | individuals. However, addressing how to ensure differential | privacy for two-party frequent itemset mining remains

关键词： Itemsets | Data mining | Databases | Protocols | Differential privacy | Mobile computing | Privacy

摘要： is that privacy leakage may become particularly | easily. To cope with these challenges, differential | privacy preservation techniques have become powerful | tools to solve the privacy leakage problem in | issues such as the balance between privacy protection

关键词： Differential privacy | Matrix factorization | Recommender system | Theoretical analysis

摘要：Federated learning enhanced with Differential | Privacy (DP) is a powerful privacy-preserving strategy | ), yielding excellent utility and privacy. Our results | with robust privacy-preserving techniques in the MRI | to protect individuals sharing their sensitive data

关键词： Federated learning | Differential privacy | Complex numbers

关键词： Diabetic retinopathy | Federated learning | Differential privacy | Privacy protection

摘要：Differential privacy (DP) is a privacy | -enhancing technology that is increasingly being used by | policy makers in the United States and Europe, but it | is not well suited to protect corporate proprietary | information that is used to produce some kinds of aggregate

关键词： Data privacy | Databases | Protection | Data models | Computer hacking | Differential privacy | Pathology | Identification of persons | Information security | Data security...

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摘要： complexity of differential* privacy, i.e., how many random* | , unbounded differential* privacy and privacy-loss parameter* | , provided the privacy-loss parameters satisfy ε, δ ≤ 1 | We initiate the study of the randomness | bits an algorithm needs in order to generate accurate

摘要：We study differential* privacy (DP) in a multi* | privacy-utility trade-off than in the well-studied local | -party setting where each party only trusts a (known | ) subset of the other parties with its data. Specifically | , given a trust graph where vertices correspond to

摘要： QNN model integrated with differential privacy | ensure differential* privacy. Our theoretical analysis* | demonstrates that in differential* privacy settings, the model* | privacy leakage risks that could undermine users | effectively protects privacy* while demonstrating good*

摘要： networks and differential* privacy, innovatively* | As the demand for privacy* protection continues* | protecting privacy. | to grow, ensuring effective financial data | processing while preventing sensitive information leakage

摘要： information. Differential* privacy has been utilized in* | to the other while achieving differential privacy | design practical methods for privacy-preserving | individuals. However, addressing how to ensure differential | privacy for two-party frequent itemset mining remains

摘要： is that privacy* leakage may become particularly* | easily. To cope with these challenges, differential | privacy preservation techniques have become powerful | tools to solve the privacy* leakage problem in* | issues such as the balance between privacy* protection*