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Sen. Netw."],"published-print":{"date-parts":[[2020,11,30]]},"abstract":"<jats:p>\n            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\u2019 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\n            <jats:italic>DAML<\/jats:italic>\n            , which to the best of our knowledge is the first attempt toward verifying participants\u2019 submissions in data aggregation based on ML. The main idea is to validate participants\u2019 submissions via SSVP, a novel secret-shared verification protocol, and then aggregate participants\u2019 parameter updates using SDA, a secure data aggregation protocol. Simulation results demonstrate that DAML can protect participants\u2019 data privacy with preferable overhead.\n          <\/jats:p>","DOI":"10.1145\/3404192","type":"journal-article","created":{"date-parts":[[2020,9,5]],"date-time":"2020-09-05T10:14:24Z","timestamp":1599300864000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["DAML"],"prefix":"10.1145","volume":"16","author":[{"given":"Ping","family":"Zhao","sequence":"first","affiliation":[{"name":"Donghua University, Shanghai, China"}]},{"given":"Jiaxin","family":"Sun","sequence":"additional","affiliation":[{"name":"Donghua University, Shanghai, China"}]},{"given":"Guanglin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Donghua University, Shanghai, China"}]}],"member":"320","published-online":{"date-parts":[[2020,9,5]]},"reference":[{"volume-title":"Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (CCS\u201916)","author":"Abadi M.","key":"e_1_2_1_1_1"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2016.2553647"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-24178-9_9"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3293537"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978331"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2018.2890609"},{"volume":"8043","volume-title":"Lecture Notes in Computer Science","author":"Ben-Sasson E.","key":"e_1_2_1_7_1"},{"volume":"7417","volume-title":"Lecture Notes in Computer Science","author":"Ben-Sasson E.","key":"e_1_2_1_8_1"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3133982"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-32946-3_15"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2019.2895769"},{"volume-title":"Proceedings of the 14th USENIX Symposium on Networked Systems Design and Implementation (NSDI\u201917)","year":"2017","author":"Corrigan-Gibbs Henry","key":"e_1_2_1_12_1"},{"volume-title":"Proceedings of the 2013 USENIX Security Symposium.","year":"2013","author":"Corrigan-Gibbs Henry","key":"e_1_2_1_13_1"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/CIS2018.2018.00098"},{"volume-title":"Proceedings of the ACM Symposium on Theory of Computing.","author":"Dwork C.","key":"e_1_2_1_15_1"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/2660267.2660280"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1515\/popets-2016-0015"},{"volume-title":"Proceedings of the International Conference on the Theory and Applications of Cryptographic Techniques.","author":"Gennaro R.","key":"e_1_2_1_18_1"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1137\/0218012"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-24676-3_27"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2015.2484326"},{"volume-title":"Is About Collecting Your Data\u2014But Not Your Data.Retrieved","year":"2020","author":"Greenberg A.","key":"e_1_2_1_22_1"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-22792-9_8"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/1182807.1182821"},{"volume-title":"Chiron: Privacy-preserving machine learning as a service. arXiv:1803.05961.","year":"2018","author":"Hunt T.","key":"e_1_2_1_25_1"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978310"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2014.2309134"},{"volume-title":"Proceedings of the 2013 IEEE INFOCOM Conference.","author":"Jung T.","key":"e_1_2_1_28_1"},{"key":"e_1_2_1_29_1","doi-asserted-by":"crossref","unstructured":"Y. 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