The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Dynamic Differential Privacy for ADMM-Based Distributed Classification Learning
Abstract
Privacy-preserving distributed machine learning becomes increasingly important due to the recent rapid growth of data. This paper focuses on a class of regularized empirical risk minimization machine learning problems, and develops two methods to provide differential privacy to distributed learning algorithms over a network. We first decentralize the learning algorithm using the alternating direction method of multipliers, and propose the methods of dual variable perturbation and primal variable perturbation to provide dynamic differential privacy. The two mechanisms lead to algorithms that can provide privacy guarantees under mild conditions of the convexity and differentiability of the loss function and the regularizer. We study the performance of the algorithms, and show that the dual variable perturbation outperforms its primal counterpart. To design an optimal privacy mechanism, we analyze the fundamental tradeoff between privacy and accuracy, and provide guidelines to choose privacy parameters. Numerical experiments using customer information database are performed to corroborate the results on privacy and utility tradeoffs and design.
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Cites
k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY
SWEENEY, L. (2002) · International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems · doi:10.1142/s0218488502001648 · Cited by 8,571
Calibrating Noise to Sensitivity in Private Data Analysis
Dwork, C., McSherry, F., Nissim, K., & Smith, A. (2006) · Lecture Notes in Computer Science · doi:10.1007/11681878_14 · Cited by 7,272
L -diversity: Privacy beyond k -anonymity
Machanavajjhala, A., Kifer, D., Gehrke, J., & Venkitasubramaniam, M. (2007) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/1217299.1217302 · Cited by 3,617
Valiant, L. G. (1984) · Communications of the ACM · doi:10.1145/1968.1972 · Cited by 3,279
Robust De-anonymization of Large Sparse Datasets
Narayanan, A., & Shmatikov, V. (2008) · 2008 IEEE Symposium on Security and Privacy (sp 2008) · doi:10.1109/sp.2008.33 · Cited by 2,372
Our Data, Ourselves: Privacy Via Distributed Noise Generation
Dwork, C., Kenthapadi, K., McSherry, F., Mironov, I., & Naor, M. (2006) · Lecture Notes in Computer Science · doi:10.1007/11761679_29 · Cited by 1,835
Cited by
Securing Fog Computing for Internet of Things Applications: Challenges and Solutions
Ni, J., Zhang, K., Lin, X., & Shen, X. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2762345 · Cited by 659
Multi-key privacy-preserving deep learning in cloud computing
Li, P., Li, J., Huang, Z., Li, T., Gao, C.-Z., Yiu, S.-M., & Chen, K. (2017) · Future Generation Computer Systems · doi:10.1016/j.future.2017.02.006 · Cited by 462
Privacy-preserving outsourced classification in cloud computing
Li, P., Li, J., Huang, Z., Gao, C.-Z., Chen, W.-B., & Chen, K. (2018) · Cluster Computing · doi:10.1007/s10586-017-0849-9 · Cited by 309
Li, Y., Wang, R., Li, Y., Zhang, M., & Long, C. (2023) · Applied Energy · doi:10.1016/j.apenergy.2022.120291 · Cited by 259
When Machine Learning Meets Privacy in 6G: A Survey
Sun, Y., Liu, J., Wang, J., Cao, Y., & Kato, N. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.3011561 · Cited by 211
Metadata
- Authors
- Zhang, T., & Zhu, Q.
- Venue
- IEEE Transactions on Information Forensics and Security
- Published
- 2017-01
- Volume
- 12
- Issue
- 1
- Pages
- 172-187
- ISSN
- 1556-6013 (ISSN-L)
- 1556-6021
- OpenAlex
- W2509467699
- Cited by
- 202
- References
- 6
- FWCI
- 10.79
- Type
- journal article
- Language
- en
- Open access
- closed
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