The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Privacy-Preserving Multivariate Statistical Analysis: Linear Regression and Classification
Open accessAbstract
Multivariate statistical analysis is an important data analysis technique that has found applications in various areas.In this paper, we study some multivariate statistical analysis methods in Secure 2-party Computation (S2C) framework illustrated by the following scenario: two parties, each having a secret data set, want to conduct the statistical analysis on their joint data, but neither party is willing to disclose its private data to the other party or any third party.The current statistical analysis techniques cannot be used directly to support this kind of computation because they require all parties to send the necessary data to a central place.In this paper, We define two Secure 2-party multivariate statistical analysis problems: Secure 2-party Multivariate Linear Regression problem and Secure 2-party Multivariate Classification problem.We have developed a practical security model, based on which we have developed a number of building blocks for solving these two problems.
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Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3298981 · Cited by 6,322
Membership Inference Attacks Against Machine Learning Models
Shokri, R., Stronati, M., Song, C., & Shmatikov, V. (2017) · 2017 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2017.41 · Cited by 4,754
Privacy-preserving deep learning
Shokri, R., & Shmatikov, V. (2015) · Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2810103.2813687 · Cited by 2,356
SecureML: A System for Scalable Privacy-Preserving Machine Learning
Mohassel, P., & Zhang, Y. (2017) · 2017 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2017.12 · Cited by 1,936
A survey on federated learning
Zhang, C., Xie, Y., Bai, H., Yu, B., Li, W., & Gao, Y. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.106775 · Cited by 1,899
Metadata
- Authors
- Du, W., Han, Y. S., & Chen, S.
- Venue
- Proceedings of the 2004 SIAM International Conference on Data Mining
- Published
- 2004-04-22
- Pages
- 222-233
- OpenAlex
- W1510952750
- Cited by
- 366
- References
- 29
- FWCI
- 9.98
- Type
- conference paper
- Language
- en
- Open access
- bronze
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