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随着大数据的发展以及数据安全相关法规的出台，人们的隐私保护意识逐渐加强，“数据孤岛”现象愈发严重。联邦学习技术作为解决该问题的有效方法之一，已成为当下备受关注的热点。在纵向联邦学习在线推理过程中，当前的主流方法并未考虑对数据标识的保护。针对此问题，提出一种适用于纵向联邦线性模型在线推理过程中的成员推断攻击的隐私保护方法，通过构造具有假阳率的过滤器来避免对数据标识的精确定位，从而保证数据的安全性；使用同态加密实现在线推理过程的全密态，保护中间计算结果；根据同态加密的密文倍乘性质，使用随机数乘法盲化操作，保证最终推理结果的安全性。该方案进一步提高了纵向联邦学习在线推理过程中用户隐私的安全性，且具有更低的计算开销和通信开销。

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