Manping Guan
Biographic Data
| ID | 10054320 |
|---|---|
| NAME | Manping Guan |
| GIVEN NAMES | Manping |
| FAMILY NAME | Guan |
| SIGNATURE | GUAN M |
| AFFILIATIONS | Anhui Normal University |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2023 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 0 |
Federated Matrix Factorization Recommendation Based on Secret Sharing for Privacy Preserving
Traditional recommendation systems require users to upload local data to the server to generate recommendation results. In this process, users’ privacy is easy to disclose. Federated recommendations can solve the problem of local data privacy leakage, but the intermediate computing results are not protected. The existing work mainly protects the information in this process through encryption or disturbance schemes, which will result in complex ca…
A Matrix Factorization Recommendation System-Based Local Differential Privacy for Protecting Users’ Sensitive Data
The recommendation system (RS) predicts user ratings by collecting user information, but the users’ private information may be exposed in this process. Thus, it is crucial to achieving a balance between recommendation performance and privacy-preserving of RSs. Aiming to solve the above problem, this article proposes a novel matrix factorization (MF) algorithm. The algorithm predicts the user rating through a linear weighting of global average rat…
No prominent works on this page.
A Matrix Factorization Recommendation System-Based Local Differential Privacy for Protecting Users’ Sensitive Data
The recommendation system (RS) predicts user ratings by collecting user information, but the users’ private information may be exposed in this process. Thus, it is crucial to achieving a balance between recommendation performance and privacy-preserving of RSs. Aiming to solve the above problem, this article proposes a novel matrix factorization (MF) algorithm. The algorithm predicts the user rating through a linear weighting of global average rat…
Federated Matrix Factorization Recommendation Based on Secret Sharing for Privacy Preserving
Traditional recommendation systems require users to upload local data to the server to generate recommendation results. In this process, users’ privacy is easy to disclose. Federated recommendations can solve the problem of local data privacy leakage, but the intermediate computing results are not protected. The existing work mainly protects the information in this process through encryption or disturbance schemes, which will result in complex ca…
Computer Science (2 works) · Computer security (2 works) · Information privacy (2 works) · Privacy-Preserving Technologies in Data (2 works) · Privacy, Security, and Data Protection (2 works) · Computer network (1 work) · Cryptography (1 work) · Cryptography and Data Security (1 work) · Data mining (1 work) · Data sharing (1 work)