分布式欺诈预防系统中的本地差分隐私嵌入模型
Locally Differentially Private Embedding Models in Distributed Fraud Prevention Systems
AI总结：
本文提出了一种基于本地差分隐私和变长交易序列潜在嵌入的协作深度学习框架，用于分布式欺诈预防系统，在保障数据隐私的同时实现了对外部模型的安全数据共享，并在真实数据集上验证了其对推理时攻击的鲁棒性及效用-隐私权衡。
摘要：
全球金融犯罪活动正在推动欺诈预防领域对机器学习解决方案的需求。然而，由于担心无意泄露和对抗性攻击，预防系统通常孤立地为金融机构提供服务，且几乎没有数据共享的规定。金融领域的协作学习进展罕见，很难找到源自隐私保护数据处理系统的真实世界见解。在本文中，我们提出了一个用于欺诈预防的协作深度学习框架，该框架从隐私角度设计，并在最近的 PETs Prize Challenges 中获奖。我们利用变长交易序列的潜在嵌入表示，结合本地差分隐私，构建了一种数据发布机制，该机制能够安全地为外部托管的欺诈和异常检测模型提供信息。我们在两个由大型支付网络捐赠的分布式数据集上评估了我们的贡献，展示了对流行推理时攻击的鲁棒性，以及与替代应用领域中已发表工作类似的效用-隐私权衡。
英文摘要：
Global financial crime activity is driving demand for machine learning solutions in fraud prevention. However, prevention systems are commonly serviced to financial institutions in isolation, and few provisions exist for data sharing due to fears of unintentional leaks and adversarial attacks. Collaborative learning advances in finance are rare, and it is hard to find real-world insights derived from privacy-preserving data processing systems. In this paper, we present a collaborative deep learning framework for fraud prevention, designed from a privacy standpoint, and awarded at the recent PETs Prize Challenges. We leverage latent embedded representations of varied-length transaction sequences, along with local differential privacy, in order to construct a data release mechanism which can securely inform externally hosted fraud and anomaly detection models. We assess our contribution on two distributed data sets donated by large payment networks, and demonstrate robustness to popular inference-time attacks, along with utility-privacy trade-offs analogous to published work in alternative application domains.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
