Privacy preserving n-party scalar product protocol
This work solves the problem of enabling secure multi-party computations for machine learning tasks, such as decision tree training, in scenarios with more than two data parties, though it is incremental as it builds on an existing two-party method.
The paper tackles the limitation of existing privacy-preserving scalar product protocols, which are mainly designed for two-party scenarios, by proposing a generalization for an arbitrary number of parties based on a recursive method. The result is a protocol that enables dot product computations across multiple decentralized datasets without revealing the data, addressing scalability and privacy concerns.
Privacy-preserving machine learning enables the training of models on decentralized datasets without the need to reveal the data, both on horizontal and vertically partitioned data. However, it relies on specialized techniques and algorithms to perform the necessary computations. The privacy preserving scalar product protocol, which enables the dot product of vectors without revealing them, is one popular example for its versatility. Unfortunately, the solutions currently proposed in the literature focus mainly on two-party scenarios, even though scenarios with a higher number of data parties are becoming more relevant. For example when performing analyses that require counting the number of samples which fulfill certain criteria defined across various sites, such as calculating the information gain at a node in a decision tree. In this paper we propose a generalization of the protocol for an arbitrary number of parties, based on an existing two-party method. Our proposed solution relies on a recursive resolution of smaller scalar products. After describing our proposed method, we discuss potential scalability issues. Finally, we describe the privacy guarantees and identify any concerns, as well as comparing the proposed method to the original solution in this aspect.