Secure multi-party machine learning allows several parties to build a model
on their pooled data to increase utility while not explicitly sharing data with
each other. We show that such multi-party computation can cause leakage of
global dataset properties between the parties even when parties obtain only
black-box access to the final model. In particular, a ``curious'' party can
infer the distribution of sensitive attributes in other parties' data with high
accuracy. This raises concerns regarding the confidentiality of properties
pertaining to the whole dataset as opposed to individual data records. We show
that our attack can leak population-level properties in datasets of different
types, including tabular, text, and graph data. To understand and measure the
source of leakage, we consider several models of correlation between a
sensitive attribute and the rest of the data. Using multiple machine learning
models, we show that leakage occurs even if the sensitive attribute is not
included in the training data and has a low correlation with other attributes
or the target variable.

该研究确认了安全多方机器学习会导致数据的属性泄露，包括敏感属性和整个数据集的属性，即使敏感属性未被训练，说明存在相关模型。

多方机器学习中数据集属性泄露

Leakage of Dataset Properties in Multi-Party Machine Learning

Machine learning relies on the availability of a vast amount of data for
training. However, in reality, most data are scattered across different
organizations and cannot be easily integrated under many legal and practical
constraints. In this paper, we introduce a new technique and framework, known
as federated transfer learning (FTL), to improve statistical models under a
data federation. The federation allows knowledge to be shared without
compromising user privacy, and enables complimentary knowledge to be
transferred in the network. As a result, a target-domain party can build more
flexible and powerful models by leveraging rich labels from a source-domain
party. A secure transfer cross validation approach is also proposed to guard
the FTL performance under the federation. The framework requires minimal
modifications to the existing model structure and provides the same level of
accuracy as the non-privacy-preserving approach. This framework is very
flexible and can be effectively adapted to various secure multi-party machine
learning tasks.

本文介绍了一种新的技术和框架，称为联邦迁移学习（FTL），它可以在数据联邦下提高统计模型的性能。该联邦允许共享知识而不会危及用户隐私，并可在网络中传输补充知识，从而使目标域方能够利用源域方的丰富标签构建更加灵活和强大的模型。同时，为了在联邦下保护 FTL 的性能，还提出了一种安全的跨验证方法。该框架需要对现有模型结构进行最小的修改，并提供与非隐私保护方法同样水平的准确性。该框架非常灵活，可有效适应各种安全多方机器学习任务。

安全联邦迁移学习

Secure Federated Transfer Learning

Federated Learning is the current state of the art in supporting secure
multi-party machine learning (ML): data is maintained on the owner's device and
the updates to the model are aggregated through a secure protocol. However,
this process assumes a trusted centralized infrastructure for coordination, and
clients must trust that the central service does not use the byproducts of
client data. In addition to this, a group of malicious clients could also harm
the performance of the model by carrying out a poisoning attack.
As a response, we propose Biscotti: a fully decentralized peer to peer (P2P)
approach to multi-party ML, which uses blockchain and cryptographic primitives
to coordinate a privacy-preserving ML process between peering clients. Our
evaluation demonstrates that Biscotti is scalable, fault tolerant, and defends
against known attacks. For example, Biscotti is able to protect the privacy of
an individual client's update and the performance of the global model at scale
when 30% of adversaries are trying to poison the model.
The implementation can be found at: this https URL

Biscotti 是一种全面去中心化的对等多方机器学习方法，使用区块链和加密原语在节点之间协调隐私保护的机器学习过程。