{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T17:12:00Z","timestamp":1778605920835,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":31,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,6,24]],"date-time":"2019-06-24T00:00:00Z","timestamp":1561334400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Key Research Program of Frontier Sciences, CAS","award":["No. QYZDY-SSW-JSC002"],"award-info":[{"award-number":["No. QYZDY-SSW-JSC002"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 61751211, 61572347, 61520106007"],"award-info":[{"award-number":["No. 61751211, 61572347, 61520106007"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key R&D Program of China","award":["2018YFB0803400"],"award-info":[{"award-number":["2018YFB0803400"]}]},{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["CNS-1526638"],"award-info":[{"award-number":["CNS-1526638"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005153","name":"China National Funds for Distinguished Young Scientists","doi-asserted-by":"publisher","award":["No. 61625205"],"award-info":[{"award-number":["No. 61625205"]}],"id":[{"id":"10.13039\/501100005153","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2019,6,24]]},"DOI":"10.1145\/3326285.3329042","type":"proceedings-article","created":{"date-parts":[[2019,6,14]],"date-time":"2019-06-14T12:42:33Z","timestamp":1560516153000},"page":"1-10","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["ML defense"],"prefix":"10.1145","author":[{"given":"Jiahui","family":"Hou","sequence":"first","affiliation":[{"name":"Univ. of Sci. and Tech. of China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianwei","family":"Qian","sequence":"additional","affiliation":[{"name":"Illinois Institute of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Wang","sequence":"additional","affiliation":[{"name":"U. of North Carolina at Charlotte"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiang-Yang","family":"Li","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haohua","family":"Du","sequence":"additional","affiliation":[{"name":"Illinois Institute of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linlin","family":"Chen","sequence":"additional","affiliation":[{"name":"Illinois Institute of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2019,6,24]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/335191.335438"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1504\/IJSN.2015.071829"},{"key":"e_1_3_2_1_3_1","volume-title":"Pros and cons of gan evaluation measures. arXiv preprint arXiv:1802.03446","author":"Borji A.","year":"2018","unstructured":"Borji , A. Pros and cons of gan evaluation measures. arXiv preprint arXiv:1802.03446 ( 2018 ). Borji, A. Pros and cons of gan evaluation measures. arXiv preprint arXiv:1802.03446 (2018)."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2015.23241"},{"key":"e_1_3_2_1_5_1","first-page":"1","article-title":"Algorithms for manifold learning. Univ. of California at San Diego Tech","volume":"12","author":"Cayton L","year":"2005","unstructured":"Cayton , L . Algorithms for manifold learning. Univ. of California at San Diego Tech . Rep 12 , 1 -- 17 ( 2005 ), 1. Cayton, L. Algorithms for manifold learning. Univ. of California at San Diego Tech. Rep 12, 1--17 (2005), 1.","journal-title":"Rep"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/SAHCN.2018.8397100"},{"key":"e_1_3_2_1_7_1","volume-title":"Applications of machine learning in cancer prediction and prognosis. Cancer informatics 2","author":"Cruz J. A.","year":"2006","unstructured":"Cruz , J. A. , and Wishart , D. S . Applications of machine learning in cancer prediction and prognosis. Cancer informatics 2 ( 2006 ), 117693510600200030. Cruz, J. A., and Wishart, D. S. Applications of machine learning in cancer prediction and prognosis. Cancer informatics 2 (2006), 117693510600200030."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1148\/rg.2017160130"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/2810103.2813677"},{"key":"e_1_3_2_1_10_1","volume-title":"Introduction to statistical pattern recognition","author":"Fukunaga K.","year":"2013","unstructured":"Fukunaga , K. Introduction to statistical pattern recognition . Elsevier , 2013 . Fukunaga, K. Introduction to statistical pattern recognition. Elsevier, 2013."},{"key":"e_1_3_2_1_11_1","first-page":"1","article-title":"the persistent topology of data","volume":"45","author":"Ghrist R.","year":"2008","unstructured":"Ghrist , R. Barcodes : the persistent topology of data . Bulletin of the AMS 45 , 1 ( 2008 ), 61--75. Ghrist, R. Barcodes: the persistent topology of data. Bulletin of the AMS 45, 1 (2008), 61--75.","journal-title":"Bulletin of the AMS"},{"key":"e_1_3_2_1_12_1","first-page":"201","volume-title":"ICML","author":"Gilad-Bachrach R.","year":"2016","unstructured":"Gilad-Bachrach , R. , Dowlin , N. , Laine , K. , Lauter , K. , Naehrig , M. , and Wernsing , J . Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy . In ICML ( 2016 ), pp. 201 -- 210 . Gilad-Bachrach, R., Dowlin, N., Laine, K., Lauter, K., Naehrig, M., and Wernsing, J. Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy. In ICML (2016), pp. 201--210."},{"key":"e_1_3_2_1_13_1","first-page":"6","article-title":"Analysis of a complex of statistical variables into principal components","volume":"24","author":"Hotelling H","year":"1933","unstructured":"Hotelling , H . Analysis of a complex of statistical variables into principal components . IJEP 24 , 6 ( 1933 ), 417. Hotelling, H. Analysis of a complex of statistical variables into principal components. IJEP 24, 6 (1933), 417.","journal-title":"IJEP"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2018.2797802"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFCOM.2013.6567070"},{"key":"e_1_3_2_1_16_1","volume-title":"Control cloud data access privilege and anonymity with fully anonymous attribute-based encryption","author":"Jung T.","year":"2015","unstructured":"Jung , T. , Li , X.-Y. , Wan , Z. , and Wan , M . Control cloud data access privilege and anonymity with fully anonymous attribute-based encryption . IEEE transactions on information forensics and security 10, 1 ( 2015 ), 190--199. Jung, T., Li, X.-Y., Wan, Z., and Wan, M. Control cloud data access privilege and anonymity with fully anonymous attribute-based encryption. IEEE transactions on information forensics and security 10, 1 (2015), 190--199."},{"key":"e_1_3_2_1_17_1","volume-title":"Geometry score: A method for comparing generative adversarial networks. arXiv preprint arXiv:1802.02664","author":"Khrulkov V.","year":"2018","unstructured":"Khrulkov , V. , and Oseledets , I . Geometry score: A method for comparing generative adversarial networks. arXiv preprint arXiv:1802.02664 ( 2018 ). Khrulkov, V., and Oseledets, I. Geometry score: A method for comparing generative adversarial networks. arXiv preprint arXiv:1802.02664 (2018)."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCC.2011.2170420"},{"key":"e_1_3_2_1_19_1","volume-title":"Umap: Uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426","author":"McInnes L.","year":"2018","unstructured":"McInnes , L. , and Healy , J . Umap: Uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426 ( 2018 ). McInnes, L., and Healy, J. Umap: Uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426 (2018)."},{"key":"e_1_3_2_1_20_1","unstructured":"McMahan H. B. Moore E. Ramage D. and Y Arcas B. A. Federated learning of deep networks using model averaging.  McMahan H. B. Moore E. Ramage D. and Y Arcas B. A. Federated learning of deep networks using model averaging."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1007\/978-0-387-70992-5_17","volume-title":"Privacy-Preserving Data Mining","author":"Nabar S. U.","year":"2008","unstructured":"Nabar , S. U. , Kenthapadi , K. , Mishra , N. , and Motwani , R . A survey of query auditing techniques for data privacy . In Privacy-Preserving Data Mining . Springer , 2008 , pp. 415 -- 431 . Nabar, S. U., Kenthapadi, K., Mishra, N., and Motwani, R. A survey of query auditing techniques for data privacy. In Privacy-Preserving Data Mining. Springer, 2008, pp. 415--431."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.5555\/1182635.1164142"},{"key":"e_1_3_2_1_23_1","first-page":"1786","volume-title":"Advances in Neural Information Processing Systems","author":"Narayanan H.","year":"2010","unstructured":"Narayanan , H. , and Mitter , S . Sample complexity of testing the manifold hypothesis . In Advances in Neural Information Processing Systems ( 2010 ), pp. 1786 -- 1794 . Narayanan, H., and Mitter, S. Sample complexity of testing the manifold hypothesis. In Advances in Neural Information Processing Systems (2010), pp. 1786--1794."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/2810103.2813687"},{"key":"e_1_3_2_1_25_1","first-page":"3","volume-title":"SP","author":"Shokri R.","year":"2017","unstructured":"Shokri , R. , Stronati , M. , Song , C. , and Shmatikov , V . Membership inference attacks against machine learning models . In SP ( 2017 ), IEEE , pp. 3 -- 18 . Shokri, R., Stronati, M., Song, C., and Shmatikov, V. Membership inference attacks against machine learning models. In SP (2017), IEEE, pp. 3--18."},{"key":"e_1_3_2_1_26_1","volume-title":"Algebraic topology-homotopy and homology","author":"Switzer R. M.","year":"2017","unstructured":"Switzer , R. M. Algebraic topology-homotopy and homology . Springer , 2017 . Switzer, R. M. Algebraic topology-homotopy and homology. Springer, 2017."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.220"},{"key":"e_1_3_2_1_28_1","volume-title":"A note on the evaluation of generative models. arXiv preprint arXiv:1511.01844","author":"Theis L.","year":"2015","unstructured":"Theis , L. , Oord , A. , and Bethge , M . A note on the evaluation of generative models. arXiv preprint arXiv:1511.01844 ( 2015 ). Theis, L., Oord, A., and Bethge, M. A note on the evaluation of generative models. arXiv preprint arXiv:1511.01844 (2015)."},{"key":"e_1_3_2_1_29_1","first-page":"601","volume-title":"USENIX","author":"Tram\u00e8r F.","year":"2016","unstructured":"Tram\u00e8r , F. , Zhang , F. , Juels , A. , Reiter , M. , and Ristenpart , T . Stealing machine learning models via prediction apis . In USENIX ( 2016 ), pp. 601 -- 618 . Tram\u00e8r, F., Zhang, F., Juels, A., Reiter, M., and Ristenpart, T. Stealing machine learning models via prediction apis. In USENIX (2016), pp. 601--618."},{"key":"e_1_3_2_1_30_1","volume-title":"AISTATS","author":"Vanhaesebrouck P.","year":"2017","unstructured":"Vanhaesebrouck , P. , Bellet , A. , and Tommasi , M . Decentralized collaborative learning of personalized models over networks . In AISTATS ( 2017 ). Vanhaesebrouck, P., Bellet, A., and Tommasi, M. Decentralized collaborative learning of personalized models over networks. In AISTATS (2017)."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2015.2470255"}],"event":{"name":"IWQoS '19: IEEE\/ACM International Symposium on Quality of Service","location":"Phoenix Arizona","acronym":"IWQoS '19"},"container-title":["Proceedings of the International Symposium on Quality of Service"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3326285.3329042","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3326285.3329042","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3326285.3329042","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:26:00Z","timestamp":1750206360000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3326285.3329042"}},"subtitle":["against prediction API threats in cloud-based machine learning service"],"short-title":[],"issued":{"date-parts":[[2019,6,24]]},"references-count":31,"alternative-id":["10.1145\/3326285.3329042","10.1145\/3326285"],"URL":"https:\/\/doi.org\/10.1145\/3326285.3329042","relation":{},"subject":[],"published":{"date-parts":[[2019,6,24]]},"assertion":[{"value":"2019-06-24","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}