Privacy-preserving Data Sharing on Vertically Partitioned Data
Razane Tajeddine, Joonas J\"alk\"o, Samuel Kaski, and Antti Honkela

TL;DR
This paper proposes a novel method combining differential privacy and secure multiparty computation to generate synthetic data from vertically partitioned datasets, ensuring privacy while maintaining data utility.
Contribution
It introduces a differentially private stochastic gradient descent algorithm for mixture models on vertically partitioned data, integrating MPC for privacy-preserving computation.
Findings
Achieved comparable accuracy to non-partitioned data on the Adult dataset.
Provided rigorous privacy guarantees for all system participants.
Demonstrated effective synthetic data generation under differential privacy constraints.
Abstract
In this work, we introduce a differentially private method for generating synthetic data from vertically partitioned data, \emph{i.e.}, where data of the same individuals is distributed across multiple data holders or parties. We present a differentially privacy stochastic gradient descent (DP-SGD) algorithm to train a mixture model over such partitioned data using variational inference. We modify a secure multiparty computation (MPC) framework to combine MPC with differential privacy (DP), in order to use differentially private MPC effectively to learn a probabilistic generative model under DP on such vertically partitioned data. Assuming the mixture components contain no dependencies across different parties, the objective function can be factorized into a sum of products of the contributions calculated by the parties. Finally, MPC is used to compute the aggregate between the…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Cryptography and Data Security · Stochastic Gradient Optimization Techniques
