{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T19:39:37Z","timestamp":1743017977795,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":40,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819722655"},{"type":"electronic","value":"9789819722662"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-97-2266-2_17","type":"book-chapter","created":{"date-parts":[[2024,4,24]],"date-time":"2024-04-24T09:02:31Z","timestamp":1713949351000},"page":"213-225","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Unlearnable Examples for\u00a0Time Series"],"prefix":"10.1007","author":[{"given":"Yujing","family":"Jiang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xingjun","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sarah Monazam","family":"Erfani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"James","family":"Bailey","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,4,25]]},"reference":[{"key":"17_CR1","doi-asserted-by":"crossref","unstructured":"Abadi, M., et al.: Deep learning with differential privacy. In: SIGSAC (2016)","DOI":"10.1145\/2976749.2978318"},{"issue":"3","key":"17_CR2","doi-asserted-by":"publisher","first-page":"683","DOI":"10.1109\/JAS.2020.1003132","volume":"7","author":"S Barra","year":"2020","unstructured":"Barra, S., Carta, S.M., Corriga, A., Podda, A.S., Recupero, D.R.: Deep learning and time series-to-image encoding for financial forecasting. IEEE\/CAA J. Automat. Sinica 7(3), 683\u2013692 (2020)","journal-title":"IEEE\/CAA J. Automat. Sinica"},{"key":"17_CR3","unstructured":"Biggio, B., Nelson, B., Laskov, P.: Poisoning attacks against support vector machines. arXiv preprint arXiv:1206.6389 (2012)"},{"key":"17_CR4","doi-asserted-by":"crossref","unstructured":"Carlini, N., Wagner, D.: Towards evaluating the robustness of neural networks. In: SP (2017)","DOI":"10.1109\/SP.2017.49"},{"key":"17_CR5","unstructured":"Croce, F., Hein, M.: Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks. arXiv preprint arXiv:2003.01690 (2020)"},{"key":"17_CR6","unstructured":"Esteban, C., Hyland, S.L., R\u00e4tsch, G.: Real-valued (medical) time series generation with recurrent conditional gans. arXiv e-prints pp. arXiv\u20131706 (2017)"},{"key":"17_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2021.116814","volume":"291","author":"Y Feng","year":"2021","unstructured":"Feng, Y., Duan, Q., Chen, X., Yakkali, S.S., Wang, J.: Space cooling energy usage prediction based on utility data for residential buildings using machine learning methods. Appl. Energy 291, 116814 (2021)","journal-title":"Appl. Energy"},{"key":"17_CR8","unstructured":"Fu, S., He, F., Liu, Y., Shen, L., Tao, D.: Robust unlearnable examples: protecting data against adversarial learning. arXiv preprint arXiv:2203.14533 (2022)"},{"key":"17_CR9","unstructured":"Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. In: ICLR (2015)"},{"key":"17_CR10","unstructured":"Hill, K.: The secretive company that might end privacy as we know it (2020)"},{"key":"17_CR11","unstructured":"Huang, H., Ma, X., Erfani, S.M., Bailey, J., Wang, Y.: Unlearnable examples: making personal data unexploitable. In: ICLR (2020)"},{"key":"17_CR12","doi-asserted-by":"crossref","unstructured":"Jiang, W., Diao, Y., Wang, H., Sun, J., Wang, M., Hong, R.: Unlearnable examples give a false sense of security: piercing through unexploitable data with learnable examples. arXiv preprint arXiv:2305.09241 (2023)","DOI":"10.1145\/3581783.3611833"},{"key":"17_CR13","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Ma, X., Erfani, S.M., Bailey, J.: Backdoor attacks on time series: a generative approach. In: SaTML (2023)","DOI":"10.1109\/SaTML54575.2023.00034"},{"key":"17_CR14","unstructured":"Koh, P.W., Liang, P.: Understanding black-box predictions via influence functions. In: ICML (2017)"},{"key":"17_CR15","unstructured":"Kurakin, A., Goodfellow, I., Bengio, S.: Adversarial machine learning at scale. arXiv preprint arXiv:1611.01236 (2016)"},{"key":"17_CR16","doi-asserted-by":"crossref","unstructured":"Li, J., et al.: Universal adversarial perturbations generative network for speaker recognition. In: ICME (2020)","DOI":"10.1109\/ICME46284.2020.9102886"},{"key":"17_CR17","doi-asserted-by":"publisher","unstructured":"Liu, Y., Ma, X., Bailey, J., Lu, F.: Reflection backdoor: a natural backdoor attack on deep neural networks. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) Computer Vision \u2013 ECCV 2020, pp. 182\u2013199. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58607-2_11","DOI":"10.1007\/978-3-030-58607-2_11"},{"key":"17_CR18","doi-asserted-by":"publisher","first-page":"352","DOI":"10.1016\/j.trc.2019.12.022","volume":"111","author":"T Ma","year":"2020","unstructured":"Ma, T., Antoniou, C., Toledo, T.: Hybrid machine learning algorithm and statistical time series model for network-wide traffic forecast. Transport. Res. Part C: Emerg. Technol. 111, 352\u2013372 (2020)","journal-title":"Transport. Res. Part C: Emerg. Technol."},{"key":"17_CR19","unstructured":"Madry, A., Makelov, A., Schmidt, L., Tsipras, D., Vladu, A.: Towards deep learning models resistant to adversarial attacks. In: ICLR (2018)"},{"key":"17_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2021.3077049","volume":"70","author":"BM Maweu","year":"2021","unstructured":"Maweu, B.M., Shamsuddin, R., Dakshit, S., Prabhakaran, B.: Generating healthcare time series data for improving diagnostic accuracy of deep neural networks. IEEE Trans. Instrum. Meas. 70, 1\u201315 (2021)","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"17_CR21","doi-asserted-by":"crossref","unstructured":"Mu\u00f1oz-Gonz\u00e1lez, L., et al.: Towards poisoning of deep learning algorithms with back-gradient optimization. In: AISec (2017)","DOI":"10.1145\/3128572.3140451"},{"key":"17_CR22","doi-asserted-by":"crossref","unstructured":"Phan, N., Wang, Y., Wu, X., Dou, D.: Differential privacy preservation for deep auto-encoders: an application of human behavior prediction. In: AAAI (2016)","DOI":"10.1609\/aaai.v30i1.10165"},{"key":"17_CR23","doi-asserted-by":"crossref","unstructured":"Phan, N., Wu, X., Hu, H., Dou, D.: Adaptive laplace mechanism: differential privacy preservation in deep learning. In: ICDM (2017)","DOI":"10.1109\/ICDM.2017.48"},{"key":"17_CR24","unstructured":"Qin, T., Gao, X., Zhao, J., Ye, K., Xu, C.Z.: Learning the unlearnable: adversarial augmentations suppress unlearnable example attacks. arXiv preprint arXiv:2303.15127 (2023)"},{"key":"17_CR25","unstructured":"Ren, J., Xu, H., Wan, Y., Ma, X., Sun, L., Tang, J.: Transferable unlearnable examples. In: ICLR (2022)"},{"key":"17_CR26","unstructured":"Shafahi, A., et al.: Poison frogs! targeted clean-label poisoning attacks on neural networks. In: NeurIPS (2018)"},{"key":"17_CR27","doi-asserted-by":"crossref","unstructured":"Shafahi, A., Najibi, M., Xu, Z., Dickerson, J., Davis, L.S., Goldstein, T.: Universal adversarial training. In: AAAI (2020)","DOI":"10.1609\/aaai.v34i04.6017"},{"key":"17_CR28","doi-asserted-by":"crossref","unstructured":"Shan, S., Ding, W., Wenger, E., Zheng, H., Zhao, B.Y.: Post-breach recovery: protection against white-box adversarial examples for leaked DNN models. In: ACM SIGSAC Conference on Computer and Communications Security (2022)","DOI":"10.1145\/3548606.3560561"},{"key":"17_CR29","unstructured":"Shan, S., Wenger, E., Zhang, J., Li, H., Zheng, H., Zhao, B.Y.: Fawkes: protecting personal privacy against unauthorized deep learning models. In: USENIX-Security (2020)"},{"key":"17_CR30","doi-asserted-by":"crossref","unstructured":"Shokri, R., Shmatikov, V.: Privacy-preserving deep learning. In: SIGSAC (2015)","DOI":"10.1145\/2810103.2813687"},{"key":"17_CR31","doi-asserted-by":"crossref","unstructured":"Shokri, R., Stronati, M., Song, C., Shmatikov, V.: Membership inference attacks against machine learning models. In: SP (2017)","DOI":"10.1109\/SP.2017.41"},{"key":"17_CR32","unstructured":"Szegedy, C., et al.: Intriguing properties of neural networks. In: ICLR (2014)"},{"key":"17_CR33","unstructured":"Wang, Y., Ma, X., Bailey, J., Yi, J., Zhou, B., Gu, Q.: On the convergence and robustness of adversarial training. In: ICML, pp. 6586\u20136595 (2019)"},{"key":"17_CR34","unstructured":"Wang, Y., Zou, D., Yi, J., Bailey, J., Ma, X., Gu, Q.: Improving adversarial robustness requires revisiting misclassified examples. In: ICLR (2020)"},{"issue":"9","key":"17_CR35","doi-asserted-by":"publisher","first-page":"1419","DOI":"10.1080\/14697688.2020.1730426","volume":"20","author":"M Wiese","year":"2020","unstructured":"Wiese, M., Knobloch, R., Korn, R., Kretschmer, P.: Quant gans: deep generation of financial time series. Quantitative Finance 20(9), 1419\u20131440 (2020)","journal-title":"Quantitative Finance"},{"key":"17_CR36","unstructured":"Wu, D., Xia, S.T., Wang, Y.: Adversarial weight perturbation helps robust generalization. Adv. Neural Inf. Process. Syst. 33 (2020)"},{"key":"17_CR37","unstructured":"Yuan, C.H., Wu, S.H.: Neural tangent generalization attacks. In: International Conference on Machine Learning, pp. 12230\u201312240. PMLR (2021)"},{"key":"17_CR38","unstructured":"Zhang, H., Yu, Y., Jiao, J., Xing, E.P., Ghaoui, L.E., Jordan, M.I.: Theoretically principled trade-off between robustness and accuracy. In: ICML (2019)"},{"key":"17_CR39","doi-asserted-by":"crossref","unstructured":"Zhang, J., et al.: Unlearnable clusters: towards label-agnostic unlearnable examples. In: CVPR (2023)","DOI":"10.1109\/CVPR52729.2023.00388"},{"key":"17_CR40","doi-asserted-by":"crossref","unstructured":"Zhao, S., Ma, X., Zheng, X., Bailey, J., Chen, J., Jiang, Y.G.: Clean-label backdoor attacks on video recognition models. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01445"}],"container-title":["Lecture Notes in Computer Science","Advances in Knowledge Discovery and Data Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-2266-2_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,24]],"date-time":"2024-04-24T23:22:40Z","timestamp":1714000960000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-2266-2_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819722655","9789819722662"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-2266-2_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"25 April 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PAKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Taipei","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Taiwan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 May 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 May 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pakdd2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/pakdd2024.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}