Laplace Input and Output Perturbation for Differentially Private Principal Components Analysis
Joint Authors
Xu, Yahong
Bai, Shuangjie
Yang, Geng
Source
Security and Communication Networks
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-11-03
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Information Technology and Computer Science
Abstract EN
With the widespread application of big data, privacy-preserving data analysis has become a topic of increasing significance.
The current research studies mainly focus on privacy-preserving classification and regression.
However, principal component analysis (PCA) is also an effective data analysis method which can be used to reduce the data dimensionality, commonly used in data processing, machine learning, and data mining.
In order to implement approximate PCA while preserving data privacy, we apply the Laplace mechanism to propose two differential privacy principal component analysis algorithms: Laplace input perturbation (LIP) and Laplace output perturbation (LOP).
We evaluate the performance of LIP and LOP in terms of noise magnitude and approximation error theoretically and experimentally.
In addition, we explore the variation of performance of the two algorithms with different parameters such as number of samples, target dimension, and privacy parameter.
Theoretical and experimental results show that algorithm LIP adds less noise and has lower approximation error than LOP.
To verify the effectiveness of algorithm LIP, we compare our LIP with other algorithms.
The experimental results show that algorithm LIP can provide strong privacy guarantee and good data utility.
American Psychological Association (APA)
Xu, Yahong& Yang, Geng& Bai, Shuangjie. 2019. Laplace Input and Output Perturbation for Differentially Private Principal Components Analysis. Security and Communication Networks،Vol. 2019, no. 2019, pp.1-10.
https://search.emarefa.net/detail/BIM-1210651
Modern Language Association (MLA)
Xu, Yahong…[et al.]. Laplace Input and Output Perturbation for Differentially Private Principal Components Analysis. Security and Communication Networks No. 2019 (2019), pp.1-10.
https://search.emarefa.net/detail/BIM-1210651
American Medical Association (AMA)
Xu, Yahong& Yang, Geng& Bai, Shuangjie. Laplace Input and Output Perturbation for Differentially Private Principal Components Analysis. Security and Communication Networks. 2019. Vol. 2019, no. 2019, pp.1-10.
https://search.emarefa.net/detail/BIM-1210651
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1210651