{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T00:47:34Z","timestamp":1760316454635,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":74,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032078834","type":"print"},{"value":"9783032078841","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T00:00:00Z","timestamp":1760313600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T00:00:00Z","timestamp":1760313600000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-07884-1_11","type":"book-chapter","created":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T16:23:07Z","timestamp":1760286187000},"page":"207-227","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Membership Privacy Evaluation in\u00a0Deep Spiking Neural Networks"],"prefix":"10.1007","author":[{"given":"Jiaxin","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gorka","family":"Abad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stjepan","family":"Picek","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mauro","family":"Conti","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,10,13]]},"reference":[{"key":"11_CR1","doi-asserted-by":"crossref","unstructured":"Abad, G., Ersoy, O., Picek, S., Ram\u00edrez-Dur\u00e1n, V.J., Urbieta, A.: Poster: backdoor attacks on spiking nns and neuromorphic datasets. In: CCS 2022, Pp. 3315\u20133317. ACM (2022)","DOI":"10.1145\/3548606.3563532"},{"key":"11_CR2","doi-asserted-by":"crossref","unstructured":"Abad, G., Ersoy, O., Picek, S., Urbieta, A.: Sneaky spikes: uncovering stealthy backdoor attacks in spiking neural networks with neuromorphic data. In: NDSS 2024 (2024)","DOI":"10.14722\/ndss.2024.24334"},{"issue":"10","key":"11_CR3","doi-asserted-by":"publisher","first-page":"1537","DOI":"10.1109\/TCAD.2015.2474396","volume":"34","author":"F Akopyan","year":"2015","unstructured":"Akopyan, F., et al.: Truenorth: design and tool flow of a 65 mw 1 million neuron programmable neurosynaptic chip. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 34(10), 1537\u20131557 (2015)","journal-title":"IEEE Trans. Comput. Aided Des. Integr. Circuits Syst."},{"issue":"6","key":"11_CR4","doi-asserted-by":"publisher","first-page":"4693","DOI":"10.1007\/s11063-021-10562-2","volume":"53","author":"D Auge","year":"2021","unstructured":"Auge, D., Hille, J., Mueller, E., Knoll, A.: A survey of encoding techniques for signal processing in spiking neural networks. Neural Process. Lett. 53(6), 4693\u20134710 (2021)","journal-title":"Neural Process. Lett."},{"key":"11_CR5","doi-asserted-by":"crossref","unstructured":"Bukaty, P.: The California Consumer Privacy Act (CCPA): An Implementation Guide. IT Governance Publishing (2019)","DOI":"10.2307\/j.ctvjghvnn"},{"key":"11_CR6","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1007\/s11263-014-0788-3","volume":"113","author":"Y Cao","year":"2015","unstructured":"Cao, Y., Chen, Y., Khosla, D.: Spiking deep convolutional neural networks for energy-efficient object recognition. Int. J. Comput. Vision 113, 54\u201366 (2015)","journal-title":"Int. J. Comput. Vision"},{"key":"11_CR7","doi-asserted-by":"crossref","unstructured":"Carlini, N., Chien, S., Nasr, M., Song, S., Terzis, A., Tram\u00e8r, F.: Membership inference attacks from first principles. In: S &P 2022, pp. 1897\u20131914. IEEE (2022)","DOI":"10.1109\/SP46214.2022.9833649"},{"issue":"4","key":"11_CR8","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1109\/MSP.2020.2985815","volume":"37","author":"G Chen","year":"2020","unstructured":"Chen, G., Cao, H., Conradt, J., Tang, H., Rohrbein, F., Knoll, A.: Event-based neuromorphic vision for autonomous driving: a paradigm shift for bio-inspired visual sensing and perception. IEEE Signal Process. Mag. 37(4), 34\u201349 (2020)","journal-title":"IEEE Signal Process. Mag."},{"key":"11_CR9","doi-asserted-by":"crossref","unstructured":"Chen, Z., Pattabiraman, K.: Overconfidence is a dangerous thing: mitigating membership inference attacks by enforcing less confident prediction. CoRR (2023)","DOI":"10.14722\/ndss.2024.23014"},{"key":"11_CR10","unstructured":"Choi, S.H.: Spiking neural networks for biomedical signal analysis. In: Biomedical Engineering Letters, pp. 1\u201312 (2024)"},{"key":"11_CR11","unstructured":"Choquette-Choo, C.A., Tramer, F., Carlini, N., Papernot, N.: Label-only membership inference attacks. In: ICML, vol.\u00a0139, pp. 1964\u20131974. PMLR (2021)"},{"key":"11_CR12","unstructured":"Conti, M., Li, J., Picek, S.: On the vulnerability of data points under multiple membership inference attacks and target models. CoRR (2022)"},{"key":"11_CR13","doi-asserted-by":"crossref","unstructured":"Conti, M., Li, J., Picek, S., Xu, J.: Label-only membership inference attack against node-level graph neural networks. In: AISec 2022, pp. 1\u201312. ACM, New York, NY, USA (2022)","DOI":"10.1145\/3560830.3563734"},{"key":"11_CR14","doi-asserted-by":"publisher","first-page":"294","DOI":"10.1016\/j.neunet.2019.09.005","volume":"121","author":"L Deng","year":"2020","unstructured":"Deng, L., et al.: Rethinking the performance comparison between snns and anns. Neural Netw. 121, 294\u2013307 (2020)","journal-title":"Neural Netw."},{"key":"11_CR15","unstructured":"DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. CoRR (2017)"},{"key":"11_CR16","doi-asserted-by":"crossref","unstructured":"Dibbo, S.V.: Sok: Model inversion attack landscape: taxonomy, challenges, and future roadmap. In: CSF 2023, pp. 439\u2013456. IEEE (2023)","DOI":"10.1109\/CSF57540.2023.00027"},{"key":"11_CR17","doi-asserted-by":"publisher","first-page":"99","DOI":"10.3389\/fncom.2015.00099","volume":"9","author":"PU Diehl","year":"2015","unstructured":"Diehl, P.U., Cook, M.: Unsupervised learning of digit recognition using spike-timing-dependent plasticity. Front. Comput. Neurosci. 9, 99 (2015)","journal-title":"Front. Comput. Neurosci."},{"key":"11_CR18","doi-asserted-by":"crossref","unstructured":"Diehl, P.U., Neil, D., Binas, J., Cook, M., Liu, S.C., Pfeiffer, M.: Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing. In: IJCNN 2015, pp.\u00a01\u20138. IEEE (2015)","DOI":"10.1109\/IJCNN.2015.7280696"},{"key":"11_CR19","doi-asserted-by":"crossref","unstructured":"Fang, W., Yu, Z., Chen, Y., Masquelier, T., Huang, T., Tian, Y.: Incorporating learnable membrane time constant to enhance learning of spiking neural networks. In: ICCV 2021, pp. 2641\u20132651. IEEE (2021)","DOI":"10.1109\/ICCV48922.2021.00266"},{"key":"11_CR20","doi-asserted-by":"crossref","unstructured":"Fang, W., et al.: Spikingjelly: an open-source machine learning infrastructure platform for spike-based intelligence. Sci. Adv. 9(40) (2023)","DOI":"10.1126\/sciadv.adi1480"},{"key":"11_CR21","first-page":"21056","volume":"34","author":"W Fang","year":"2021","unstructured":"Fang, W., Yu, Z., Chen, Y., Huang, T., Masquelier, T., Tian, Y.: Deep residual learning in spiking neural networks. NeurIPS 34, 21056\u201321069 (2021)","journal-title":"NeurIPS"},{"issue":"4","key":"11_CR22","doi-asserted-by":"publisher","first-page":"594","DOI":"10.1109\/TPAMI.2006.79","volume":"28","author":"L Fei-Fei","year":"2006","unstructured":"Fei-Fei, L., Fergus, R., Perona, P.: One-shot learning of object categories. IEEE Trans. Pattern Anal. Mach. Intell. 28(4), 594\u2013611 (2006)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"37","key":"11_CR23","doi-asserted-by":"publisher","first-page":"11628","DOI":"10.1523\/JNEUROSCI.23-37-11628.2003","volume":"23","author":"N Fourcaud-Trocm\u00e9","year":"2003","unstructured":"Fourcaud-Trocm\u00e9, N., Hansel, D., Van Vreeswijk, C., Brunel, N.: How spike generation mechanisms determine the neuronal response to fluctuating inputs. J. Neurosci. 23(37), 11628\u201311640 (2003)","journal-title":"J. Neurosci."},{"key":"11_CR24","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press (2016)"},{"key":"11_CR25","doi-asserted-by":"crossref","unstructured":"Gu, F., Sng, W., Hu, X., Yu, F.: Eventdrop: data augmentation for event-based learning. CoRR (2021)","DOI":"10.24963\/ijcai.2021\/97"},{"key":"11_CR26","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1109\/JEDS.2024.3366199","volume":"12","author":"A Gupta","year":"2024","unstructured":"Gupta, A., Saurabh, S.: Unsupervised learning in a ternary snn using stdp. IEEE J. Electron Dev. Soc. 12, 211\u2013220 (2024)","journal-title":"IEEE J. Electron Dev. Soc."},{"key":"11_CR27","first-page":"133","volume":"2019","author":"J Hayes","year":"2019","unstructured":"Hayes, J., Melis, L., Danezis, G., Cristofaro, E.D.: LOGAN: membership inference attacks against generative models. PETS 2019, 133\u2013152 (2019)","journal-title":"PETS"},{"key":"11_CR28","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR 2016, pp. 770\u2013778. IEEE (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"11_CR29","doi-asserted-by":"crossref","unstructured":"He, X., Zhang, Y.: Quantifying and mitigating privacy risks of contrastive learning. In: Kim, Y., Kim, J., Vigna, G., Shi, E. (eds.) CCS 2021, pp. 845\u2013863. ACM (2021)","DOI":"10.1145\/3460120.3484571"},{"key":"11_CR30","doi-asserted-by":"crossref","unstructured":"Hui, B., Yang, Y., Yuan, H., Burlina, P., Gong, N.Z., Cao, Y.: Practical blind membership inference attack via differential comparisons. In: NDSS 2021, The Internet Society (2021)","DOI":"10.14722\/ndss.2021.24293"},{"key":"11_CR31","unstructured":"Hunsberger, E., Eliasmith, C.: Spiking deep networks with LIF neurons. CoRR (2015)"},{"issue":"6","key":"11_CR32","doi-asserted-by":"publisher","first-page":"1569","DOI":"10.1109\/TNN.2003.820440","volume":"14","author":"EM Izhikevich","year":"2003","unstructured":"Izhikevich, E.M.: Simple model of spiking neurons. IEEE Trans. Neural Networks 14(6), 1569\u20131572 (2003)","journal-title":"IEEE Trans. Neural Networks"},{"key":"11_CR33","doi-asserted-by":"crossref","unstructured":"Jia, J., Salem, A., Backes, M., Zhang, Y., Gong, N.Z.: MemGuard: defending against black-box membership inference attacks via adversarial examples. In: CCS 2019, pp. 259\u2013274. ACM (2019)","DOI":"10.1145\/3319535.3363201"},{"key":"11_CR34","unstructured":"Kong, F., et al.: An efficient membership inference attack for the diffusion model by proximal initialization. CoRR (2023)"},{"key":"11_CR35","unstructured":"Krizhevsky, A., Hinton, G., et\u00a0al.: Learning multiple layers of features from tiny images (2009)"},{"key":"11_CR36","first-page":"25","volume":"2012","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. NeurIPS 2012, 25 (2012)","journal-title":"NeurIPS"},{"issue":"11","key":"11_CR37","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y Lecun","year":"1998","unstructured":"Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998)","journal-title":"Proc. IEEE"},{"issue":"7553","key":"11_CR38","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436\u2013444 (2015)","journal-title":"Nature"},{"key":"11_CR39","doi-asserted-by":"crossref","unstructured":"Lee, J.H., Delbruck, T., Pfeiffer, M.: Training deep spiking neural networks using backpropagation. Front. Neurosci. 10 (2016)","DOI":"10.3389\/fnins.2016.00508"},{"key":"11_CR40","doi-asserted-by":"publisher","first-page":"1418115","DOI":"10.3389\/fncom.2024.1418115","volume":"18","author":"F Lei","year":"2024","unstructured":"Lei, F., Yang, X., Liu, J., Dou, R., Wu, N.: Dt-scnn: dual-threshold spiking convolutional neural network with fewer operations and memory access for edge applications. Front. Comput. Neurosci. 18, 1418115 (2024)","journal-title":"Front. Comput. Neurosci."},{"key":"11_CR41","doi-asserted-by":"publisher","first-page":"309","DOI":"10.3389\/fnins.2017.00309","volume":"11","author":"H Li","year":"2017","unstructured":"Li, H., Liu, H., Ji, X., Li, G., Shi, L.: Cifar10-dvs: an event-stream dataset for object classification. Front. Neurosci. 11, 309 (2017)","journal-title":"Front. Neurosci."},{"key":"11_CR42","doi-asserted-by":"crossref","unstructured":"Li, Y., Kim, Y., Park, H., Geller, T., Panda, P.: Neuromorphic data augmentation for training spiking neural networks. In: ECCV 2022, pp. 631\u2013649. Springer-Verlag (2022)","DOI":"10.1007\/978-3-031-20071-7_37"},{"key":"11_CR43","doi-asserted-by":"crossref","unstructured":"Li, Z., Zhang, Y.: Membership leakage in label-only exposures. In: CCS 2021, pp. 880\u2013895. ACM (2021)","DOI":"10.1145\/3460120.3484575"},{"issue":"5","key":"11_CR44","doi-asserted-by":"publisher","first-page":"2569","DOI":"10.1109\/TNNLS.2021.3106961","volume":"34","author":"L Liang","year":"2021","unstructured":"Liang, L., et al.: Exploring adversarial attack in spiking neural networks with spike-compatible gradient. IEEE Trans. Neural Netw. Learn. Syst. 34(5), 2569\u20132583 (2021)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"11_CR45","doi-asserted-by":"crossref","unstructured":"Liu, H., Jia, J., Qu, W., Gong, N.Z.: Encodermi: membership inference against pre-trained encoders in contrastive learning. In: CCS 2021, pp. 2081\u20132095. ACM (2021)","DOI":"10.1145\/3460120.3484749"},{"key":"11_CR46","doi-asserted-by":"crossref","unstructured":"Long, Y., et al.: A pragmatic approach to membership inferences on machine learning models. In: EuroS &P 2020, pp. 521\u2013534. IEEE (2020)","DOI":"10.1109\/EuroSP48549.2020.00040"},{"key":"11_CR47","doi-asserted-by":"crossref","unstructured":"Lotfi\u00a0Rezaabad, A., Vishwanath, S.: Long short-term memory spiking networks and their applications. In: ICONS 2020, pp.\u00a01\u20139. ACM (2020)","DOI":"10.1145\/3407197.3407211"},{"issue":"2","key":"11_CR48","doi-asserted-by":"publisher","first-page":"024004","DOI":"10.1088\/2634-4386\/ad3a95","volume":"4","author":"S Lu","year":"2024","unstructured":"Lu, S., Sengupta, A.: Deep unsupervised learning using spike-timing-dependent plasticity. Neuromorphic Comput. Eng. 4(2), 024004 (2024)","journal-title":"Neuromorphic Comput. Eng."},{"key":"11_CR49","doi-asserted-by":"crossref","unstructured":"Nasr, M., Shokri, R., Houmansadr, A.: Machine learning with membership privacy using adversarial regularization. In: CCS 2018, pp. 634\u2013646. ACM (2018)","DOI":"10.1145\/3243734.3243855"},{"issue":"6","key":"11_CR50","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1109\/MSP.2019.2931595","volume":"36","author":"EO Neftci","year":"2019","unstructured":"Neftci, E.O., Mostafa, H., Zenke, F.: Surrogate gradient learning in spiking neural networks: bringing the power of gradient-based optimization to spiking neural networks. IEEE Signal Process. Mag. 36(6), 51\u201363 (2019)","journal-title":"IEEE Signal Process. Mag."},{"issue":"9","key":"11_CR51","first-page":"3640","volume":"69","author":"O Nomura","year":"2022","unstructured":"Nomura, O., Sakemi, Y., Hosomi, T., Morie, T.: Robustness of spiking neural networks based on time-to-first-spike encoding against adversarial attacks. IEEE Trans. Circuits Syst. II Express Briefs 69(9), 3640\u20133644 (2022)","journal-title":"IEEE Trans. Circuits Syst. II Express Briefs"},{"key":"11_CR52","doi-asserted-by":"publisher","first-page":"437","DOI":"10.3389\/fnins.2015.00437","volume":"9","author":"G Orchard","year":"2015","unstructured":"Orchard, G., Jayawant, A., Cohen, G.K., Thakor, N.: Converting static image datasets to spiking neuromorphic datasets using saccades. Front. Neurosci. 9, 437 (2015)","journal-title":"Front. Neurosci."},{"key":"11_CR53","doi-asserted-by":"publisher","first-page":"409662","DOI":"10.3389\/fnins.2018.00774","volume":"12","author":"M Pfeiffer","year":"2018","unstructured":"Pfeiffer, M., Pfeil, T.: Deep learning with spiking neurons: opportunities and challenges. Front. Neurosci. 12, 409662 (2018)","journal-title":"Front. Neurosci."},{"issue":"16","key":"11_CR54","doi-asserted-by":"publisher","first-page":"13187","DOI":"10.1007\/s00521-021-06824-8","volume":"34","author":"D Po\u0142ap","year":"2022","unstructured":"Po\u0142ap, D., Wo\u017aniak, M., Ho\u0142ubowski, W., Dama\u0161evi\u010dius, R.: A heuristic approach to the hyperparameters in training spiking neural networks using spike-timing-dependent plasticity. Neural Comput. Appl. 34(16), 13187\u201313200 (2022)","journal-title":"Neural Comput. Appl."},{"key":"11_CR55","unstructured":"Rueckauer, B., Lungu, I.A., Hu, Y., Pfeiffer, M.: Theory and tools for the conversion of analog to spiking convolutional neural networks. CoRR (2016)"},{"key":"11_CR56","doi-asserted-by":"publisher","first-page":"294078","DOI":"10.3389\/fnins.2017.00682","volume":"11","author":"B Rueckauer","year":"2017","unstructured":"Rueckauer, B., Lungu, I.A., Hu, Y., Pfeiffer, M., Liu, S.C.: Conversion of continuous-valued deep networks to efficient event-driven networks for image classification. Front. Neurosci. 11, 294078 (2017)","journal-title":"Front. Neurosci."},{"key":"11_CR57","doi-asserted-by":"crossref","unstructured":"Salem, A., Zhang, Y., Humbert, M., Berrang, P., Fritz, M., Backes, M.: ML-leaks: model and data independent membership inference attacks and defenses on machine learning models. In: NDSS 2019, The Internet Society (2019)","DOI":"10.14722\/ndss.2019.23119"},{"key":"11_CR58","doi-asserted-by":"crossref","unstructured":"Sengupta, A., Ye, Y., Wang, R., Liu, C., Roy, K.: Going deeper in spiking neural networks: vgg and residual architectures. Front. Neurosci. 13 (2019)","DOI":"10.3389\/fnins.2019.00095"},{"key":"11_CR59","doi-asserted-by":"crossref","unstructured":"Sharmin, S., Panda, P., Sarwar, S.S., Lee, C., Ponghiran, W., Roy, K.: A comprehensive analysis on adversarial robustness of spiking neural networks. In: IJCNN 2019, pp.\u00a01\u20138. IEEE (2019)","DOI":"10.1109\/IJCNN.2019.8851732"},{"key":"11_CR60","doi-asserted-by":"crossref","unstructured":"Shokri, R., Stronati, M., Song, C., Shmatikov, V.: Membership inference attacks against machine learning models. In: S &P 2017, pp. 3\u201318. IEEE (2017)","DOI":"10.1109\/SP.2017.41"},{"key":"11_CR61","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR (2014)"},{"key":"11_CR62","unstructured":"Song, L., Mittal, P.: Systematic evaluation of privacy risks of machine learning models. In: USENIX Security 2021, pp. 2615\u20132632. USENIX Association (2021)"},{"key":"11_CR63","doi-asserted-by":"crossref","unstructured":"Taigman, Y., Yang, M., Ranzato, M., Wolf, L.: Deepface: closing the gap to human-level performance in face verification. In: CVPR 2014, pp. 1701\u20131708. IEEE (2014)","DOI":"10.1109\/CVPR.2014.220"},{"key":"11_CR64","unstructured":"TEAM, I.G.P.: EU General Data Protection Regulation (GDPR): An Implementation and Compliance Guide - Second edition. IT Governance Publishing, 2 edn. (2017)"},{"issue":"4","key":"11_CR65","doi-asserted-by":"publisher","first-page":"2649","DOI":"10.1007\/s11063-021-10514-w","volume":"53","author":"RE Turkson","year":"2021","unstructured":"Turkson, R.E., Qu, H., Mawuli, C.B., Eghan, M.J.: Classification of alzheimer\u2019s disease using deep convolutional spiking neural network. Neural Process. Lett. 53(4), 2649\u20132663 (2021)","journal-title":"Neural Process. Lett."},{"key":"11_CR66","unstructured":"Wang, H., Li, Y.F., Gryllias, K.: Brain-inspired spiking neural networks for industrial fault diagnosis: a survey, challenges, and opportunities. CoRR (2023)"},{"key":"11_CR67","doi-asserted-by":"crossref","unstructured":"Wu, Y., Deng, L., Li, G., Zhu, J., Shi, L.: Spatio-temporal backpropagation for training high-performance spiking neural networks. Front. Neurosci. 12 (2018)","DOI":"10.3389\/fnins.2018.00331"},{"key":"11_CR68","doi-asserted-by":"publisher","first-page":"1527","DOI":"10.1016\/j.procs.2023.08.138","volume":"221","author":"L Xiaoxue","year":"2023","unstructured":"Xiaoxue, L., et al.: Review of medical data analysis based on spiking neural networks. Procedia Comput. Sci. 221, 1527\u20131538 (2023)","journal-title":"Procedia Comput. Sci."},{"key":"11_CR69","doi-asserted-by":"crossref","unstructured":"Yaghini, M., Kulynych, B., Cherubin, G., Troncoso, C.: Disparate vulnerability: on the unfairness of privacy attacks against machine learning. In: PETS, pp. 460\u2013480 (2022)","DOI":"10.2478\/popets-2022-0023"},{"key":"11_CR70","doi-asserted-by":"crossref","unstructured":"Ye, J., Maddi, A., Murakonda, S.K., Bindschaedler, V., Shokri, R.: Enhanced membership inference attacks against machine learning models. In: CCS 2022, p. 3093\u20133106. ACM (2022)","DOI":"10.1145\/3548606.3560675"},{"key":"11_CR71","doi-asserted-by":"crossref","unstructured":"Yeom, S., Giacomelli, I., Fredrikson, M., Jha, S.: Privacy risk in machine learning: analyzing the connection to overfitting. In: CSF 2018, pp. 268\u2013282. IEEE (2018)","DOI":"10.1109\/CSF.2018.00027"},{"key":"11_CR72","doi-asserted-by":"crossref","unstructured":"Yun, S., Han, D., Oh, S.J., Chun, S., Choe, J., Yoo, Y.: Cutmix: regularization strategy to train strong classifiers with localizable features. In: CVPR 2019, pp. 6023\u20136032. IEEE (2019)","DOI":"10.1109\/ICCV.2019.00612"},{"key":"11_CR73","doi-asserted-by":"crossref","unstructured":"Zheng, H., Wu, Y., Deng, L., Hu, Y., Li, G.: Going deeper with directly-trained larger spiking neural networks. In: AAAI 2021, vol.\u00a035, pp. 11062\u201311070 (2021)","DOI":"10.1609\/aaai.v35i12.17320"},{"issue":"3","key":"11_CR74","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1109\/JPROC.2023.3238524","volume":"111","author":"Z Zou","year":"2023","unstructured":"Zou, Z., Chen, K., Shi, Z., Guo, Y., Ye, J.: Object detection in 20 years: a survey. Proc. IEEE 111(3), 257\u2013276 (2023)","journal-title":"Proc. IEEE"}],"container-title":["Lecture Notes in Computer Science","Computer Security \u2013 ESORICS 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-07884-1_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T16:23:17Z","timestamp":1760286197000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-07884-1_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,13]]},"ISBN":["9783032078834","9783032078841"],"references-count":74,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-07884-1_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,13]]},"assertion":[{"value":"13 October 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ESORICS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Symposium on Research in Computer Security","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Toulouse","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"esorics2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.esorics2025.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}