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

Published: 05 September 2020 Publication History

Abstract

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.

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  • (2023)An Improved Traffic Forecasting Model based on Efficient Spatiotemporal Graph Convolutional NetworkProceedings of the 2023 12th International Conference on Informatics, Environment, Energy and Applications10.1145/3594692.3594700(48-51)Online publication date: 17-Feb-2023
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  1. DAML: Practical Secure Protocol for Data Aggregation Based on Machine Learning

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      cover image ACM Transactions on Sensor Networks
      ACM Transactions on Sensor Networks  Volume 16, Issue 4
      November 2020
      311 pages
      ISSN:1550-4859
      EISSN:1550-4867
      DOI:10.1145/3414039
      Issue’s Table of Contents
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Publication History

      Published: 05 September 2020
      Accepted: 01 May 2020
      Revised: 01 May 2020
      Received: 01 July 2019
      Published in TOSN Volume 16, Issue 4

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      Author Tags

      1. Malformed message attacks
      2. data aggregation
      3. data privacy
      4. machine learning

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      • Fundamental Research Funds for the Central Universities
      • Shanghai Sailing Program
      • National Natural Science Foundation of China
      • Shanghai Rising-Star Program

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      View all
      • (2023)An Improved Traffic Forecasting Model based on Efficient Spatiotemporal Graph Convolutional NetworkProceedings of the 2023 12th International Conference on Informatics, Environment, Energy and Applications10.1145/3594692.3594700(48-51)Online publication date: 17-Feb-2023
      • (2021)Garbage In, Garbage Out: Poisoning Attacks Disguised With Plausible Mobility in Data AggregationIEEE Transactions on Network Science and Engineering10.1109/TNSE.2021.31039198:3(2679-2693)Online publication date: 1-Jul-2021
      • (2021)Computational Task Offloading Scheme based on Deep Learning for Financial Big Data2021 16th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)10.1109/ISKE54062.2021.9755398(647-654)Online publication date: 26-Nov-2021

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