Data aggregation based on machine learning (ML), in mobile edge computing, allows participants to send ephemeral parameter updates of local ML on their private data instead of the exact data to the untrusted aggregator. However, it still enables the untrusted aggregator to reconstruct participants’ private data, although parameter updates contain significantly less information than the private data. Existing work either incurs extremely high overhead or ignores malicious participants dropping out. The latest research deals with the dropouts with desirable cost, but it is vulnerable to malformed message attacks. To this end, we focus on the data aggregation based on ML in a practical setting where malicious participants may send malformed parameter updates to perturb the total parameter updates learned by the aggregator. Moreover, malicious participants may drop out and collude with other participants or the untrusted aggregator. In such a scenario, we propose a scheme named DAML , which to the best of our knowledge is the first attempt toward verifying participants’ submissions in data aggregation based on ML. The main idea is to validate participants’ submissions via SSVP, a novel secret-shared verification protocol, and then aggregate participants’ parameter updates using SDA, a secure data aggregation protocol. Simulation results demonstrate that DAML can protect participants’ data privacy with preferable overhead.

中文翻译：

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DAML

在移动边缘计算中，基于机器学习 (ML) 的数据聚合允许参与者在其私有数据上发送本地 ML 的临时参数更新，而不是向不受信任的聚合器发送确切的数据。然而，它仍然使不受信任的聚合器能够重建参与者的私有数据，尽管参数更新包含的信息明显少于私有数据。现有的工作要么会产生极高的开销，要么会忽略恶意参与者的退出。最新的研究以理想的成本处理辍学，但它容易受到格式错误的消息攻击。为此，我们专注于在实际环境中基于 ML 的数据聚合，其中恶意参与者可能会发送格式错误的参数更新以扰乱聚合器学习的总参数更新。而且，恶意参与者可能会退出并与其他参与者或不受信任的聚合器勾结。在这种情况下，我们提出了一个名为DAML，据我们所知，这是在基于 ML 的数据聚合中验证参与者提交的第一次尝试。主要思想是通过 SSVP（一种新颖的秘密共享验证协议）验证参与者的提交，然后使用安全数据聚合协议 SDA 聚合参与者的参数更新。仿真结果表明，DAML 可以以更好的开销保护参与者的数据隐私。