Privacy-Preserving Deep Learning via Additively Homomorphic Encryption
Abstract
We present a privacy-preserving deep learning system in which many learning participants perform neural network-based deep learning over a combined dataset of all, without revealing the participants' local data to a central server. To that end, we revisit the previous work by Shokri and Shmatikov (ACM CCS 2015) and show that, with their method, local data information may be leaked to an honest-but-curious server. We then fix that problem by building an enhanced system with the following properties: 1) no information is leaked to the server and 2) accuracy is kept intact, compared with that of the ordinary deep learning system also over the combined dataset. Our system bridges deep learning and cryptography: we utilize asynchronous stochastic gradient descent as applied to neural networks, in combination with additively homomorphic encryption. We show that our usage of encryption adds tolerable overhead to the ordinary deep learning system.
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Malicious hacker attacks and dishonest participants may use the model parameters to infer the original data.
“For example, malicious hacker attacks and dishonest participants may use the model parameters to infer the original data (Phong et al. 2017).”
From Survey: federated learning data security and privacy-preserving in edge-Internet of Things (2024) · cites this paper as Phong et al. 2017
The processes of uploading and downloading the model update parameters, training iterations, and other processes still expose the FL environment to a series of risks.
“However, the processes of uploading and downloading the model update parameters, training iterations, and other processes still expose the FL environment to a series of risks.”
From Survey: federated learning data security and privacy-preserving in edge-Internet of Things (2024) · cites this paper as Phong et al. 2017
Even a small portion of gradients can reveal a fair amount of sensitive information about the local data.
“For instance, as mentioned in [31], even a small portion of gradients can reveal a fair amount of sensitive information about the local data.”
From Privacy and Robustness in Federated Learning: Attacks and Defenses (2024) · cites this paper as [31]
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- Authors
- Phong, L. T., Aono, Y., Hayashi, T., Wang, L., & Moriai, S.
- Affiliations
- National Institute of Information and Communications Technology (JP)
- Kobe University (JP)
- Venue
- IEEE Transactions on Information Forensics and Security
- Published
- 2018-05
- Volume
- 13
- Issue
- 5
- Pages
- 1333-1345
- ISSN
- 1556-6013 (ISSN-L)
- 1556-6021
- OpenAlex
- W2781091734
- Cited by
- 1,662
- References
- 23
- Citations by year
- 2026: 115 · 2025: 255 · 2024: 314 · 2023: 275 · 2022: 248 · 2021: 227 · 2020: 149 · 2019: 65 · 2018: 14
- FWCI
- 37.19
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Information Forensics and Security (publishedVersion)
- Funders
- Japan Science and Technology Agency CREST (JPMJCR168A)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Cryptography and Data Security
- Physical Sciences › Computer Science › Information Systems › Cloud Data Security Solutions
- Keywords
- privacy-preserving deep learning, honest-but-curious server, data leakage, asynchronous stochastic gradient descent, artificial neural networks
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