{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T10:39:17Z","timestamp":1769855957091,"version":"3.49.0"},"reference-count":16,"publisher":"Association for Computing Machinery (ACM)","issue":"5","license":[{"start":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T00:00:00Z","timestamp":1696032000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2023,10,31]]},"abstract":"<jats:p>\n            Obfuscating a dataset by adding random noises to protect the privacy of sensitive samples in the training dataset is crucial to prevent data leakage to untrusted parties when dataset sharing is essential. We conduct comprehensive experiments to investigate how the dataset obfuscation can affect the resultant model weights \u2014in terms of the model accuracy, \u2113\n            <jats:sup>2<\/jats:sup>\n            -distance-based model distance, and level of data privacy\u2014and discuss the potential applications with the proposed Privacy, Utility, and Distinguishability (PUD)-triangle diagram to visualize the requirement preferences. Our experiments are based on the popular MNIST and CIFAR-10 datasets under both independent and identically distributed (IID) and non-IID settings. Significant results include a tradeoff between the model accuracy and privacy level and a tradeoff between the model difference and privacy level. The results indicate broad application prospects for training outsourcing and guarding against attacks in federated learning both of which have been increasingly attractive in many areas, particularly learning in edge computing.\n          <\/jats:p>","DOI":"10.1145\/3597936","type":"journal-article","created":{"date-parts":[[2023,5,23]],"date-time":"2023-05-23T12:00:27Z","timestamp":1684843227000},"page":"1-15","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Obfuscating the Dataset: Impacts and Applications"],"prefix":"10.1145","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6111-1607","authenticated-orcid":false,"given":"Guangsheng","family":"Yu","sequence":"first","affiliation":[{"name":"Data61, CSIRO, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9439-6437","authenticated-orcid":false,"given":"Xu","family":"Wang","sequence":"additional","affiliation":[{"name":"FEIT, UTS, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1529-3179","authenticated-orcid":false,"given":"Caijun","family":"Sun","sequence":"additional","affiliation":[{"name":"Zhejiang Lab, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8913-873X","authenticated-orcid":false,"given":"Ping","family":"Yu","sequence":"additional","affiliation":[{"name":"Faculty of Computing, Harbin Institute of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0780-4637","authenticated-orcid":false,"given":"Wei","family":"Ni","sequence":"additional","affiliation":[{"name":"Data61, CSIRO, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7001-6305","authenticated-orcid":false,"given":"Ren Ping","family":"Liu","sequence":"additional","affiliation":[{"name":"FEIT, UTS, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,9,30]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.2018.1700202"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2019.2921977"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1145\/2810103.2813687"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1515\/popets-2018-0024"},{"key":"e_1_3_1_7_2","first-page":"619","volume-title":"Proceedings of the 25th USENIX Security Symposium (USENIX Security 16)","author":"Ohrimenko Olga","year":"2016","unstructured":"Olga Ohrimenko, Felix Schuster, Cedric Fournet, Aastha Mehta, Sebastian Nowozin, Kapil Vaswani, and Manuel Costa. 2016. Oblivious multi-party machine learning on trusted processors. In Proceedings of the 25th USENIX Security Symposium (USENIX Security 16). USENIX Association, Austin, TX, 619\u2013636. 978-1-931971-32-4https:\/\/www.usenix.org\/conference\/usenixsecurity16\/technical-sessions\/presentation\/ohrimenko."},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2015.23241"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1145\/3214303"},{"key":"e_1_3_1_10_2","article-title":"Privacy-preserving machine learning through data obfuscation","volume":"1807","author":"Zhang Tianwei","year":"2018","unstructured":"Tianwei Zhang, Zecheng He, and Ruby B. Lee. 2018. Privacy-preserving machine learning through data obfuscation. CoRR abs\/1807.01860 (2018). arXiv:1807.01860. http:\/\/arxiv.org\/abs\/1807.01860.","journal-title":"CoRR"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3134077"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/SP40001.2021.00106"},{"key":"e_1_3_1_13_2","volume-title":"Advances in Neural Information Processing Systems","author":"Blanchard Peva","year":"2017","unstructured":"Peva Blanchard, El Mahdi El Mhamdi, Rachid Guerraoui, and Julien Stainer. 2017. Machine learning with adversaries: Byzantine tolerant gradient descent. In Advances in Neural Information Processing Systems, I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.), Vol. 30. Curran Associates, Inc. https:\/\/proceedings.neurips.cc\/paper\/2017\/file\/f4b9ec30ad9f68f89b29639786cb62ef-Paper.pdf."},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_3_1_15_2","unstructured":"Alex Krizhevsky. 2009. Learning multiple layers of features from tiny images. University of Toronto."},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2019.2942179"},{"key":"e_1_3_1_17_2","article-title":"Federated learning on non-iid data silos: An experimental study","author":"Li Qinbin","year":"2021","unstructured":"Qinbin Li, Yiqun Diao, Quan Chen, and Bingsheng He. 2021. Federated learning on non-iid data silos: An experimental study. arXiv preprint arXiv:2102.02079 (2021).","journal-title":"arXiv preprint arXiv:2102.02079"}],"container-title":["ACM Transactions on Intelligent Systems and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3597936","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3597936","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:37:59Z","timestamp":1750178279000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3597936"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,30]]},"references-count":16,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2023,10,31]]}},"alternative-id":["10.1145\/3597936"],"URL":"https:\/\/doi.org\/10.1145\/3597936","relation":{},"ISSN":["2157-6904","2157-6912"],"issn-type":[{"value":"2157-6904","type":"print"},{"value":"2157-6912","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,30]]},"assertion":[{"value":"2022-09-18","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-05-12","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-09-30","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}