Efficient and Privacy Preserving Group Signature for Federated Learning
- URL: http://arxiv.org/abs/2207.05297v1
- Date: Tue, 12 Jul 2022 04:12:10 GMT
- Title: Efficient and Privacy Preserving Group Signature for Federated Learning
- Authors: Sneha Kanchan, Jae Won Jang, Jun Yong Yoon, Bong Jun Choi
- Abstract summary: Federated Learning (FL) is a Machine Learning (ML) technique that aims to reduce the threats to user data privacy.
This paper proposes an efficient and privacy-preserving protocol for FL based on group signature.
- Score: 2.121963121603413
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Federated Learning (FL) is a Machine Learning (ML) technique that aims to
reduce the threats to user data privacy. Training is done using the raw data on
the users' device, called clients, and only the training results, called
gradients, are sent to the server to be aggregated and generate an updated
model. However, we cannot assume that the server can be trusted with private
information, such as metadata related to the owner or source of the data. So,
hiding the client information from the server helps reduce privacy-related
attacks. Therefore, the privacy of the client's identity, along with the
privacy of the client's data, is necessary to make such attacks more difficult.
This paper proposes an efficient and privacy-preserving protocol for FL based
on group signature. A new group signature for federated learning, called GSFL,
is designed to not only protect the privacy of the client's data and identity
but also significantly reduce the computation and communication costs
considering the iterative process of federated learning. We show that GSFL
outperforms existing approaches in terms of computation, communication, and
signaling costs. Also, we show that the proposed protocol can handle various
security attacks in the federated learning environment.
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