45,747 research outputs found
Rmind: a tool for cryptographically secure statistical analysis
Secure multi-party computation platforms are becoming more and more practical. This has paved the way for privacy-preserving statistical analysis using secure multi-party computation. Simple statistical analysis functions have been emerging here and there in literature, but no comprehensive system has been compiled. We describe and implement the most used statistical analysis functions in the privacy-preserving setting including simple statistics, t-test, test, Wilcoxon tests and linear regression. We give descriptions of the privacy-preserving algorithms and benchmark results that show the feasibility of our solution
EFMVFL: An Efficient and Flexible Multi-party Vertical Federated Learning without a Third Party
Federated learning allows multiple participants to conduct joint modeling
without disclosing their local data. Vertical federated learning (VFL) handles
the situation where participants share the same ID space and different feature
spaces. In most VFL frameworks, to protect the security and privacy of the
participants' local data, a third party is needed to generate homomorphic
encryption key pairs and perform decryption operations. In this way, the third
party is granted the right to decrypt information related to model parameters.
However, it isn't easy to find such a credible entity in the real world.
Existing methods for solving this problem are either communication-intensive or
unsuitable for multi-party scenarios. By combining secret sharing and
homomorphic encryption, we propose a novel VFL framework without a third party
called EFMVFL, which supports flexible expansion to multiple participants with
low communication overhead and is applicable to generalized linear models. We
give instantiations of our framework under logistic regression and Poisson
regression. Theoretical analysis and experiments show that our framework is
secure, more efficient, and easy to be extended to multiple participants.Comment: 9pages,2 figure
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