DAML: Practical Secure Protocol for Data Aggregation Based on Machine Learning

Data aggregation based on machine learning (ML), in Mobile Edge Computing (MEC), 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 DAML, which is the first attempt toward verifying participants’ submissions in Date Aggregation based on ML. The main idea is to validate participants’ submissions via novel secret-shared verification protocol SSVP and then aggregate participants’ parameter updates using secure data aggregation protocol SDA. Simulation results demonstrate that DAML can protect participants’ data privacy with preferable overhead (Zhao P, Sun J, Zhang G. DAML: Practical secure protocol for data aggregation based on machine learning[J]. ACM Transactions on Sensor Networks (TOSN), 2020, 16(4): 1–18. https://doi.org/10.1145/3404192 .).