1. 横向联邦学习中 PCA 差分隐私数据发布算法.
- Author
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朱 骁 and 杨 庚
- Subjects
- *
DATA release , *COVARIANCE matrices , *DATA reduction , *PRIVACY , *NOISE , *RANDOM matrices - Abstract
To allow different organizations to jointly use PCA for dimensionality reduction and data release under the premise of protecting the privacy of local sensitive data and the data released after dimensionality reduction, this paper proposed a horizontal federated PCA differential privacy data publishing algorithm. It introduced a random seed joint negotiation scheme to generate the same noise matrix between sites with less communication cost. It proposed a local noise averaging scheme, and added the averaging noise to the local covariance matrix. On the one hand, it protected local data privacy. On the other hand, it reduced the amount of noise added and achieved the same noise level as the centralized differential privacy PCA algorithm. Compared with similar algorithms, the amount of noise added was reduced. The experiment evaluates the algorithm from the perspective of privacy and utility, and proves that the algorithm has higher utility compared with similar algorithms. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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