Secure Federated Analytics on Vertically Partitioned Data

该项目的目标是通过分析交易信息来提高算法检测金融犯罪的准确性，同时确保个人和组织的隐私得到保护。金融犯罪的规模是巨大的：联合国估计，每年有8000亿至20000亿美元被洗钱，占全球GDP的2-5%。一个更成功的检测方法可以减少这一数额，以及当真正的账户交易被标记为潜在欺诈时所造成的费用和不便。可以通过不同金融机构的合作来提高准确性，将它们所掌握的有关所涉及的账户和交易的信息拼凑在一起，建立一个更清晰的特征和模式，表明欺诈。然而，链接在一起的信息也导致更丰富的个人资料，为每笔交易和每个帐户。该项目旨在开发一种方法，对不同金融机构持有的数据进行分析，以提高犯罪侦查的准确性，而无需将数据收集和集中在一个地方。联邦学习方法用于从数据中获得预测特征，并在不共享机密个人记录的情况下训练机器学习模型。这些组织能够共同创建一个高精度的模型，然后可以部署该模型来监控和标记潜在的问题交易。该项目旨在实现协作分析，同时防止机密信息在金融机构之间共享，并限制通过部署机器学习模型可以了解到的有关无辜个人的任何信息。

The goal of this project is to improve the accuracy of algorithms to detect financial crime by analysing transaction information, while at the same time ensuring that the privacy of individuals and organisations is protected. The scale of the financial crime is vast: the UN estimates that US$800-2000bn is laundered each year, representing 2-5% of global GDP. A more successful approach for detection can reduce this amount as well as the expense and inconvenience caused when genuine account transactions are flagged as potentially fraudulent.Accuracy can be improved through the collaboration of different financial institutions to piece together the information they hold about the accounts and transactions involved, building up a clearer picture of the characteristics and patterns that indicate fraud.However linking together information also results in richer profiles for each transaction and each account. This can increase privacy risk by providing more possibilities to recognise individuals in the dataset and reveal their sensitive information.The project seeks to develop an approach to carry out analysis on the data held across different financial institutions to improve the accuracy of crime detection without collecting and centralising the data one place. A federated learning approach is used to derive predictive features from the data and to train a machine learning model without sharing confidential individual records. Together the organisations are able to produce a high-accuracy model which can then be deployed to monitor and flag potentially problematic transactions.The project seeks to enable collaborative analysis while preventing confidential information being shared across financial institutions and to limit any information that can be learned about innocent individuals from deployment of the machine learning model.