Building a Cloud-Native Privacy Computing Platform in Financial Industry - Yiyang Jin & Lecheng Gong
在金融行业构建一个云原生隐私计算平台 | Building a Cloud-Native Privacy Computing Platform in Financial Industry - Yiyang Jin & Lecheng Gong, Shanghai Pudong Development Bank
上海浦东发展银行(SPDB)基于Linux基金会的FATE项目构建了其隐私计算平台“波塞冬”。随着对数据隐私、数据安全和数据本地化的监管日益严格,该平台采用云原生框架来更好地管理、部署和扩展联邦学习系统,同时在数据安全和算法性能之间取得平衡。该平台现在作为SPDB在行业数据协作中的关键节点,并在电信反欺诈、信用风险评估和信用卡营销等各种业务场景中发挥关键作用。在本次演讲中,演讲者将分享SPDB在构建云原生联邦学习平台方面的经验,包括:1. 在SPDB上将FATE改造为Kubernetes。2. 构建云原生网络和存储的最佳实践。3. 隐私计算的云原生安全实践。4. 数据拓扑导向的Pod调度创新。
Shanghai Pudong Development Bank (SPDB) built its privacy computing platform "Poseidon" based on the Linux Foundation's FATE project. With the increasingly strict regulation of data privacy, data security, and data localization, the platform adopts the cloud-native framework to better manage, deploy and scale up the federated learning systems while balancing between data security and algorithm performance. The platform now serves as a key node for the SPDB in data collaboration across the industry and plays a critical role in various business scenarios including telecom anti-fraud, credit risk assessment, and credit card marketing. In this session, the speaker will share the journey of SPDB in building a cloud-native federated learning platform, which includes: 1. Revamping FATE on Kubernetes at SPDB 2. Best practices in building cloud-native networking and storage. 3. Practices in cloud-native security for privacy computing. 4. Innovations on data-topology-oriented pod scheduling.
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