{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T17:21:06Z","timestamp":1742923266423,"version":"3.40.3"},"publisher-location":"Cham","reference-count":38,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030630751"},{"type":"electronic","value":"9783030630768"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-63076-8_4","type":"book-chapter","created":{"date-parts":[[2020,11,25]],"date-time":"2020-11-25T20:03:24Z","timestamp":1606334604000},"page":"51-65","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Task-Agnostic Privacy-Preserving Representation Learning via Federated Learning"],"prefix":"10.1007","author":[{"given":"Ang","family":"Li","sequence":"first","affiliation":[]},{"given":"Huanrui","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Yiran","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,11,26]]},"reference":[{"key":"4_CR1","unstructured":"Avent, B., Korolova, A., Zeber, D., Hovden, T., Livshits, B.: Blender: enabling local search with a hybrid differential privacy model. In: 26th USENIX Security Symposium (USENIX Security 17), pp. 747\u2013764 (2017)"},{"key":"4_CR2","unstructured":"Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)"},{"key":"4_CR3","doi-asserted-by":"crossref","unstructured":"Bassily, R., Smith, A.: Local, private, efficient protocols for succinct histograms. In: Proceedings of the Forty-seventh Annual ACM Symposium on Theory of Computing, pp. 127\u2013135 (2015)","DOI":"10.1145\/2746539.2746632"},{"key":"4_CR4","doi-asserted-by":"crossref","unstructured":"Blodgett, S.L., Green, L., O\u2019Connor, B.: Demographic dialectal variation in social media: a case study of African-american English. arXiv preprint arXiv:1608.08868 (2016)","DOI":"10.18653\/v1\/D16-1120"},{"key":"4_CR5","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"4_CR6","unstructured":"Dosovitskiy, A., Brox, T.: Generating images with perceptual similarity metrics based on deep networks. In: Advances in Neural Information Processing Systems, pp. 658\u2013666 (2016)"},{"key":"4_CR7","doi-asserted-by":"crossref","unstructured":"Dosovitskiy, A., Brox, T.: Inverting visual representations with convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4829\u20134837 (2016)","DOI":"10.1109\/CVPR.2016.522"},{"key":"4_CR8","doi-asserted-by":"crossref","unstructured":"Duchi, J.C., Jordan, M.I., Wainwright, M.J.: Local privacy and statistical minimax rates. In: 2013 IEEE 54th Annual Symposium on Foundations of Computer Science, pp. 429\u2013438. IEEE (2013)","DOI":"10.1109\/FOCS.2013.53"},{"key":"4_CR9","doi-asserted-by":"crossref","unstructured":"Erlingsson, U., Pihur, V., Korolova, A.: RAPPOR: randomized aggregatable privacy-preserving ordinal response. In: Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security, pp. 1054\u20131067 (2014)","DOI":"10.1145\/2660267.2660348"},{"key":"4_CR10","unstructured":"Gilad-Bachrach, R., Dowlin, N., Laine, K., Lauter, K., Naehrig, M., Wernsing, J.: CryptoNets: applying neural networks to encrypted data with high throughput and accuracy. In: International Conference on Machine Learning, pp. 201\u2013210 (2016)"},{"key":"4_CR11","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"4_CR12","unstructured":"Hjelm, R.D., et al.: Learning deep representations by mutual information estimation and maximization. arXiv preprint arXiv:1808.06670 (2018)"},{"key":"4_CR13","unstructured":"Kim, T.h., Kang, D., Pulli, K., Choi, J.: Training with the invisibles: obfuscating images to share safely for learning visual recognition models. arXiv preprint arXiv:1901.00098 (2019)"},{"key":"4_CR14","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv:1412.6980, December 2014"},{"key":"4_CR15","unstructured":"Konecny, J., McMahan, H.B., Yu, F.X., Richtarik, P., Suresh, A.T., Bacon, D.: Federated learning: strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492 (2016)"},{"key":"4_CR16","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097\u20131105 (2012)"},{"key":"4_CR17","doi-asserted-by":"publisher","unstructured":"Li, A., Duan, Y., Yang, H., Chen, Y., Yang, J.: TIPRDC: task-independent privacy-respecting data crowdsourcing framework for deep learning with anonymized intermediate representations. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2020, pp. 824\u2013832. Association for Computing Machinery, New York (2020). https:\/\/doi.org\/10.1145\/3394486.3403125","DOI":"10.1145\/3394486.3403125"},{"key":"4_CR18","unstructured":"Li, A., Guo, J., Yang, H., Chen, Y.: DeepObfuscator: adversarial training framework for privacy-preserving image classification. arXiv preprint arXiv:1909.04126 (2019)"},{"key":"4_CR19","doi-asserted-by":"crossref","unstructured":"Li, N., Li, T., Venkatasubramanian, S.: t-closeness: privacy beyond k-anonymity and l-diversity. In: 2007 IEEE 23rd International Conference on Data Engineering, pp. 106\u2013115. IEEE (2007)","DOI":"10.1109\/ICDE.2007.367856"},{"key":"4_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision \u2013 ECCV 2014","author":"T-Y Lin","year":"2014","unstructured":"Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48"},{"issue":"4","key":"4_CR21","first-page":"1","volume":"3","author":"S Liu","year":"2019","unstructured":"Liu, S., Du, J., Shrivastava, A., Zhong, L.: Privacy adversarial network: representation learning for mobile data privacy. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 3(4), 1\u201318 (2019)","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"4_CR22","doi-asserted-by":"crossref","unstructured":"Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: Proceedings of International Conference on Computer Vision (ICCV), December 2015","DOI":"10.1109\/ICCV.2015.425"},{"key":"4_CR23","doi-asserted-by":"crossref","unstructured":"Mahendran, A., Vedaldi, A.: Understanding deep image representations by inverting them. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5188\u20135196 (2015)","DOI":"10.1109\/CVPR.2015.7299155"},{"key":"4_CR24","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., y Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273\u20131282 (2017)"},{"key":"4_CR25","unstructured":"Nowozin, S., Cseke, B., Tomioka, R.: f-GAN: training generative neural samplers using variational divergence minimization. In: Advances in Neural Information Processing Systems, pp. 271\u2013279 (2016)"},{"key":"4_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/978-3-319-46487-9_2","volume-title":"Computer Vision \u2013 ECCV 2016","author":"SJ Oh","year":"2016","unstructured":"Oh, S.J., Benenson, R., Fritz, M., Schiele, B.: Faceless person recognition: privacy implications in social media. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 19\u201335. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46487-9_2"},{"key":"4_CR27","doi-asserted-by":"crossref","unstructured":"Oh, S.J., Fritz, M., Schiele, B.: Adversarial image perturbation for privacy protection a game theory perspective. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 1491\u20131500. IEEE (2017)","DOI":"10.1109\/ICCV.2017.165"},{"key":"4_CR28","unstructured":"Oord, A.v.d., et al.: WaveNet: a generative model for raw audio. arXiv preprint arXiv:1609.03499 (2016)"},{"key":"4_CR29","doi-asserted-by":"publisher","first-page":"4505","DOI":"10.1109\/JIOT.2020.2967734","volume":"7","author":"SA Osia","year":"2020","unstructured":"Osia, S.A., et al.: A hybrid deep learning architecture for privacy-preserving mobile analytics. IEEE Internet Things J. 7, 4505\u20134518 (2020)","journal-title":"IEEE Internet Things J."},{"key":"4_CR30","unstructured":"Peng, X.B., Kanazawa, A., Toyer, S., Abbeel, P., Levine, S.: Variational discriminator bottleneck: improving imitation learning, inverse RL, and GANs by constraining information flow. arXiv preprint arXiv:1810.00821 (2018)"},{"key":"4_CR31","doi-asserted-by":"crossref","unstructured":"Pittaluga, F., Koppal, S., Chakrabarti, A.: Learning privacy preserving encodings through adversarial training. In: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 791\u2013799. IEEE (2019)","DOI":"10.1109\/WACV.2019.00089"},{"key":"4_CR32","doi-asserted-by":"crossref","unstructured":"Qin, Z., Yang, Y., Yu, T., Khalil, I., Xiao, X., Ren, K.: Heavy hitter estimation over set-valued data with local differential privacy. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, pp. 192\u2013203 (2016)","DOI":"10.1145\/2976749.2978409"},{"key":"4_CR33","doi-asserted-by":"crossref","unstructured":"Smith, A., Thakurta, A., Upadhyay, J.: Is interaction necessary for distributed private learning? In: 2017 IEEE Symposium on Security and Privacy (SP), pp. 58\u201377. IEEE (2017)","DOI":"10.1109\/SP.2017.35"},{"key":"4_CR34","unstructured":"Song, J., Kalluri, P., Grover, A., Zhao, S., Ermon, S.: Learning controllable fair representations. arXiv preprint arXiv:1812.04218 (2018)"},{"issue":"05","key":"4_CR35","doi-asserted-by":"publisher","first-page":"557","DOI":"10.1142\/S0218488502001648","volume":"10","author":"L Sweeney","year":"2002","unstructured":"Sweeney, L.: k-anonymity: a model for protecting privacy. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 10(05), 557\u2013570 (2002)","journal-title":"Int. J. Uncertain. Fuzziness Knowl. Based Syst."},{"key":"4_CR36","unstructured":"Wu, Y., et al.: Google\u2019s neural machine translation system: bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144 (2016)"},{"key":"4_CR37","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"627","DOI":"10.1007\/978-3-030-01270-0_37","volume-title":"Computer Vision \u2013 ECCV 2018","author":"Z Wu","year":"2018","unstructured":"Wu, Z., Wang, Z., Wang, Z., Jin, H.: Towards privacy-preserving visual recognition via adversarial training: a pilot study. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11220, pp. 627\u2013645. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01270-0_37"},{"key":"4_CR38","doi-asserted-by":"crossref","unstructured":"Yonetani, R., Naresh Boddeti, V., Kitani, K.M., Sato, Y.: Privacy-preserving visual learning using doubly permuted homomorphic encryption. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2040\u20132050 (2017)","DOI":"10.1109\/ICCV.2017.225"}],"container-title":["Lecture Notes in Computer Science","Federated Learning"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-63076-8_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,11,25]],"date-time":"2020-11-25T20:07:37Z","timestamp":1606334857000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-63076-8_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030630751","9783030630768"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-63076-8_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"26 November 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}