{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T07:16:34Z","timestamp":1763968594910,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":71,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T00:00:00Z","timestamp":1730678400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006374","name":"National Science Foundation","doi-asserted-by":"publisher","award":["2220434,2220433"],"award-info":[{"award-number":["2220434,2220433"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,11,4]]},"DOI":"10.1145\/3646547.3689011","type":"proceedings-article","created":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T09:40:26Z","timestamp":1730454026000},"page":"545-554","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["N\n            <scp>et<\/scp>\n            DPS\n            <scp>yn<\/scp>\n            : Synthesizing Network Traces under Differential Privacy"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-0066-3459","authenticated-orcid":false,"given":"Danyu","family":"Sun","sequence":"first","affiliation":[{"name":"University of California, Irvine, Irvine, CA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-0787-058X","authenticated-orcid":false,"given":"Joann Qiongna","family":"Chen","sequence":"additional","affiliation":[{"name":"San Diego State University, San Diego, CA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6178-4118","authenticated-orcid":false,"given":"Chen","family":"Gong","sequence":"additional","affiliation":[{"name":"University of Virginia, Charlottesville, VA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9017-7947","authenticated-orcid":false,"given":"Tianhao","family":"Wang","sequence":"additional","affiliation":[{"name":"University of Virginia, Charlottesville, VA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9401-1012","authenticated-orcid":false,"given":"Zhou","family":"Li","sequence":"additional","affiliation":[{"name":"University of California, Irvine, Irvine, CA, USA"}]}],"member":"320","published-online":{"date-parts":[[2024,11,4]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"2020. PGM code repo. https:\/\/github.com\/ryan112358\/private-pgm."},{"key":"e_1_3_2_1_2_1","unstructured":"2022. The CAIDA UCSD Anonymized Internet Traces. https:\/\/www.caida.org\/catalog\/datasets\/passive_dataset."},{"key":"e_1_3_2_1_3_1","unstructured":"2022. Capture files from Mid-Atlantic CCDC. https:\/\/www.netresec.com\/?page= MACCDC."},{"key":"e_1_3_2_1_4_1","unstructured":"2023. NetShare code repo. https:\/\/github.com\/netsharecmu\/NetShare."},{"key":"e_1_3_2_1_5_1","unstructured":"2023. PrivMRF code repo. https:\/\/github.com\/caicre\/PrivMRF."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.29012\/jpc.686"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"crossref","unstructured":"M Bagnulo P Matthews and I van Beijnum. 2011. Stateful NAT64: Network Address and Protocol Translation from IPv6 Clients to IPv4 Servers. Technical Report.","DOI":"10.17487\/rfc6146"},{"key":"e_1_3_2_1_9_1","volume-title":"Proceedings of the 10th ACM SIGCOMM Internet Measurement Conference, IMC 2010","author":"Benson Theophilus","year":"2010","unstructured":"Theophilus Benson, Aditya Akella, and David A. Maltz. 2010. Network traffic characteristics of data centers in the wild. In Proceedings of the 10th ACM SIGCOMM Internet Measurement Conference, IMC 2010, Melbourne, Australia - November 1-3, 2010, Mark Allman (Ed.). ACM, 267--280."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/1879141.1879175"},{"volume-title":"Time series analysis: forecasting and control","author":"Box George EP","key":"e_1_3_2_1_11_1","unstructured":"George EP Box, Gwilym M Jenkins, Gregory C Reinsel, and Greta M Ljung. 2015. Time series analysis: forecasting and control. John Wiley & Sons."},{"key":"e_1_3_2_1_12_1","volume-title":"TCC 2016-B (Lecture Notes in Computer Science","volume":"658","author":"Bun Mark","year":"2016","unstructured":"Mark Bun and Thomas Steinke. 2016. Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds. In Theory of Cryptography - 14th International Conference, TCC 2016-B (Lecture Notes in Computer Science, Vol. 9985), Martin Hirt and Adam D. Smith (Eds.). 635--658."},{"key":"e_1_3_2_1_13_1","unstructured":"Philipp B\u00f6nninghausen. 2024. Intrusion Detection Datasets: An overview of datasets suitable for IDS research. https:\/\/fkie-cad.github.io\/intrusion-detectiondatasets\/content\/all_datasets\/."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.14778\/3476249.3476272"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589287"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP46214.2022.9833649"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.2478\/popets-2019-0003"},{"key":"e_1_3_2_1_18_1","volume-title":"Electronics and Mobile Communication Conference (IEMCON). IEEE, 0728--0734","author":"Cheng Adriel","year":"2019","unstructured":"Adriel Cheng. 2019. PAC-GAN: Packet generation of network traffic using generative adversarial networks. In 2019 IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). IEEE, 0728--0734."},{"key":"e_1_3_2_1_19_1","volume-title":"Workshop on Overcoming Measurement Barriers to Internet Research (WOMBIR 2021)","author":"Claffy KC","year":"2021","unstructured":"KC Claffy, David Clark, John Heidemann, Fabian Bustamante, Mattijs Jonker, Aaron Schulman, and Ellen Zegura. 2021. Workshop on Overcoming Measurement Barriers to Internet Research (WOMBIR 2021) Final Report. ACM SIGCOMM Computer Communication Review 51, 3 (2021), 33--40."},{"key":"e_1_3_2_1_20_1","volume-title":"Linear program reconstruction in practice. arXiv preprint arXiv:1810.05692","author":"Cohen Aloni","year":"2018","unstructured":"Aloni Cohen and Kobbi Nissim. 2018. Linear program reconstruction in practice. arXiv preprint arXiv:1810.05692 (2018)."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jalgor.2003.12.001"},{"key":"e_1_3_2_1_22_1","volume-title":"Synthetic Data: Methods, Use Cases, and Risks","author":"Cristofaro Emiliano De","year":"2024","unstructured":"Emiliano De Cristofaro. 2024. Synthetic Data: Methods, Use Cases, and Risks. IEEE Security & Privacy (2024)."},{"key":"e_1_3_2_1_23_1","volume-title":"Differentially Private Diffusion Models. Transactions on Machine Learning Research","author":"Dockhorn Tim","year":"2023","unstructured":"Tim Dockhorn, Tianshi Cao, Arash Vahdat, and Karsten Kreis. 2023. Differentially Private Diffusion Models. Transactions on Machine Learning Research (2023). https:\/\/openreview.net\/forum?id=ZPpQk7FJXF"},{"key":"e_1_3_2_1_24_1","volume-title":"LDPTrace: Locally Differentially Private Trajectory Synthesis. arXiv preprint arXiv:2302.06180","author":"Du Yuntao","year":"2023","unstructured":"Yuntao Du, Yujia Hu, Zhikun Zhang, Ziquan Fang, Lu Chen, Baihua Zheng, and Yunjun Gao. 2023. LDPTrace: Locally Differentially Private Trajectory Synthesis. arXiv preprint arXiv:2302.06180 (2023)."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1007\/11681878_14"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-statistics-060116-054123"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-81242-3_1"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/FOCS.2018.00056"},{"key":"e_1_3_2_1_29_1","volume-title":"Laurent Gomez, and Patrick Duverger.","author":"Frigerio Lorenzo","year":"2019","unstructured":"Lorenzo Frigerio, Anderson Santana de Oliveira, Laurent Gomez, and Patrick Duverger. 2019. Differentially Private Generative Adversarial Networks for Time Series, Continuous, and Discrete Open Data. In ICT Systems Security and Privacy Protection, Vol. 562. Springer, 151--164."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.2478\/popets-2021-0040"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3287287"},{"key":"e_1_3_2_1_32_1","volume-title":"Differentially Private Diffusion Models Generate Useful Synthetic Images. CoRR abs\/2302.13861","author":"Ghalebikesabi Sahra","year":"2023","unstructured":"Sahra Ghalebikesabi, Leonard Berrada, Sven Gowal, Ira Ktena, Robert Stanforth, Jamie Hayes, Soham De, Samuel L. Smith, Olivia Wiles, and Borja Balle. 2023. Differentially Private Diffusion Models Generate Useful Synthetic Images. CoRR abs\/2302.13861 (2023)."},{"key":"e_1_3_2_1_33_1","first-page":"20172","article-title":"User-level differentially private learning via correlated sampling","volume":"34","author":"Ghazi Badih","year":"2021","unstructured":"Badih Ghazi, Ravi Kumar, and Pasin Manurangsi. 2021. User-level differentially private learning via correlated sampling. Advances in Neural Information Processing Systems 34 (2021), 20172--20184.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2924492"},{"key":"e_1_3_2_1_35_1","unstructured":"Florimond Houssiau James Jordon Samuel N. Cohen Owen Daniel Andrew Elliott James Geddes Callum Mole Camila Rangel-Smith and Lukasz Szpruch. 2022. TAPAS: a Toolbox for Adversarial Privacy Auditing of Synthetic Data. arXiv:2211.06550 [cs.CR] https:\/\/arxiv.org\/abs\/2211.06550"},{"key":"e_1_3_2_1_36_1","volume-title":"Chang Ge, Bolin Ding, David Forsyth, Bo Li, and Dawn Song.","author":"Hu Yuzheng","year":"2023","unstructured":"Yuzheng Hu, Fan Wu, Qinbin Li, Yunhui Long, Gonzalo Munilla Garrido, Chang Ge, Bolin Ding, David Forsyth, Bo Li, and Dawn Song. 2023. SoK: Privacy- Preserving Data Synthesis. arXiv preprint arXiv:2307.02106 (2023)."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3472305.3472324"},{"key":"e_1_3_2_1_38_1","volume-title":"Paul Schmitt, Francesco Bronzino, and Nick Feamster.","author":"Jiang Xi","year":"2023","unstructured":"Xi Jiang, Shinan Liu, Aaron Gember-Jacobson, Arjun Nitin Bhagoji, Paul Schmitt, Francesco Bronzino, and Nick Feamster. 2023. NetDiffusion: Network Data Augmentation Through Protocol-Constrained Traffic Generation. arXiv preprint arXiv:2310.08543 (2023)."},{"key":"e_1_3_2_1_39_1","volume-title":"International conference on extending database technology","volume":"2014","author":"Li Haoran","year":"2014","unstructured":"Haoran Li, Li Xiong, and Xiaoqian Jiang. 2014. Differentially private synthesization of multi-dimensional data using copula functions. In Advances in database technology: proceedings. International conference on extending database technology, Vol. 2014. NIH Public Access, 475."},{"key":"e_1_3_2_1_40_1","unstructured":"Kecen Li Chen Gong Zhixiang Li Yuzhong Zhao Xinwen Hou and Tianhao Wang. 2023. Meticulously Selecting 1% of the Dataset for Pretraining! Generating Differentially Private Images Data with Semantics Query. arXiv:2311.12850 [cs.CV]"},{"key":"e_1_3_2_1_41_1","volume-title":"International Conference on Learning Representations. https:\/\/openreview.net\/for um?id=M6M8BEmd6dq","author":"Liew Seng Pei","year":"2022","unstructured":"Seng Pei Liew, Tsubasa Takahashi, and Michihiko Ueno. 2022. PEARL: Data Synthesis via Private Embeddings and Adversarial Reconstruction Learning. In International Conference on Learning Representations. https:\/\/openreview.net\/for um?id=M6M8BEmd6dq"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3419394.3423643"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/3341302.3342076"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/2934872.2934906"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2017.11.004"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.14778\/3551793.3551817"},{"key":"e_1_3_2_1_47_1","first-page":"20696","article-title":"Relaxed marginal consistency for differentially private query answering","volume":"34","author":"McKenna Ryan","year":"2021","unstructured":"Ryan McKenna, Siddhant Pradhan, Daniel R Sheldon, and Gerome Miklau. 2021. Relaxed marginal consistency for differentially private query answering. Advances in Neural Information Processing Systems 34 (2021), 20696--20707.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_48_1","volume-title":"Proceedings of the 36th International Conference on Machine Learning, ICML (Proceedings of Machine Learning Research","volume":"4444","author":"McKenna Ryan","year":"2019","unstructured":"Ryan McKenna, Daniel Sheldon, and Gerome Miklau. 2019. Graphical-model based estimation and inference for differential privacy. In Proceedings of the 36th International Conference on Machine Learning, ICML (Proceedings of Machine Learning Research, Vol. 97). PMLR, 4435--4444."},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/1851275.1851199"},{"key":"e_1_3_2_1_50_1","volume-title":"European Symposium on Research in Computer Security. Springer, 380--399","author":"Meeus Matthieu","year":"2023","unstructured":"Matthieu Meeus, Florent Guepin, Ana-Maria Cre\u0163u, and Yves-Alexandre de Montjoye. 2023. Achilles? heels: vulnerable record identification in synthetic data publishing. In European Symposium on Research in Computer Security. Springer, 380--399."},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.scs.2021.102994"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1109\/DSN.2015.14"},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/2588555.2588575"},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2018.12.012"},{"key":"e_1_3_2_1_55_1","first-page":"41","article-title":"Creation of flow-based data sets for intrusion detection","volume":"16","author":"Ring Markus","year":"2017","unstructured":"Markus Ring, Sarah Wunderlich, Dominik Gr\u00fcdl, Dieter Landes, and Andreas Hotho. 2017. Creation of flow-based data sets for intrusion detection. Journal of Information Warfare 16, 4 (2017), 41--54.","journal-title":"Journal of Information Warfare"},{"key":"e_1_3_2_1_56_1","volume-title":"NetDiffus: Network Traffic Generation by Diffusion Models through Time-Series Imaging. arXiv preprint arXiv:2310.04429","author":"Sivaroopan Nirhoshan","year":"2023","unstructured":"Nirhoshan Sivaroopan, Dumindu Bandara, Chamara Madarasingha, Guilluame Jourjon, Anura Jayasumana, and Kanchana Thilakarathna. 2023. NetDiffus: Network Traffic Generation by Diffusion Models through Time-Series Imaging. arXiv preprint arXiv:2310.04429 (2023)."},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1109\/LCN58197.2023.10223392"},{"key":"e_1_3_2_1_58_1","volume-title":"31st USENIX Security Symposium (USENIX Security 22)","author":"Stadler Theresa","year":"2022","unstructured":"Theresa Stadler, Bristena Oprisanu, and Carmela Troncoso. 2022. Synthetic data--anonymisation groundhog day. In 31st USENIX Security Symposium (USENIX Security 22). 1451--1468."},{"key":"e_1_3_2_1_59_1","unstructured":"Xinyu Tang Richard Shin Huseyin A. Inan Andre Manoel Fatemehsadat Mireshghallah Zinan Lin Sivakanth Gopi Janardhan Kulkarni and Robert Sim. 2023. Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation. arXiv:2309.11765 [cs.LG]"},{"key":"e_1_3_2_1_60_1","volume-title":"USENIX Security Symposium","author":"Wang Haiming","year":"2023","unstructured":"Haiming Wang, Zhikun Zhang, Tianhao Wang, Shibo He, Michael Backes, Jiming Chen, and Yang Zhang. 2023. PrivTrace: Differentially Private Trajectory Synthesis by Adaptive Markov Model. In USENIX Security Symposium 2023."},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICC40277.2020.9148946"},{"key":"e_1_3_2_1_62_1","volume-title":"10th IEEE International Conference on Network Protocols, 2002. Proceedings. IEEE, 280--289","author":"Xu Jun","year":"2002","unstructured":"Jun Xu, Jinliang Fan, Mostafa H Ammar, and Sue B Moon. 2002. Prefix-preserving ip address anonymization: Measurement-based security evaluation and a new cryptography-based scheme. In 10th IEEE International Conference on Network Protocols, 2002. Proceedings. IEEE, 280--289."},{"key":"e_1_3_2_1_63_1","volume-title":"Modeling tabular data using conditional gan. Advances in neural information processing systems 32","author":"Xu Lei","year":"2019","unstructured":"Lei Xu, Maria Skoularidou, Alfredo Cuesta-Infante, and Kalyan Veeramachaneni. 2019. Modeling tabular data using conditional gan. Advances in neural information processing systems 32 (2019)."},{"key":"e_1_3_2_1_64_1","volume-title":"A comparative study of network traffic representations for novelty detection. arXiv preprint arXiv:2006.16993","author":"Yang Kun","year":"2020","unstructured":"Kun Yang, Samory Kpotufe, and Nick Feamster. 2020. A comparative study of network traffic representations for novelty detection. arXiv preprint arXiv:2006.16993 (2020)."},{"volume-title":"Privacy risk in machine learning: Analyzing the connection to overfitting. In 2018 IEEE 31st computer security foundations symposium (CSF)","author":"Yeom Samuel","key":"e_1_3_2_1_65_1","unstructured":"Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha. 2018. Privacy risk in machine learning: Analyzing the connection to overfitting. In 2018 IEEE 31st computer security foundations symposium (CSF). IEEE, 268--282."},{"key":"e_1_3_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.1145\/3544216.3544251"},{"volume-title":"PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees. In International Conference on Learning Representations.","author":"Yoon Jinsung","key":"e_1_3_2_1_67_1","unstructured":"Jinsung Yoon, James Jordon, and Mihaela van der Schaar. 2019. PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_68_1","volume-title":"32nd USENIX Security Symposium, USENIX Security","author":"Yuan Quan","year":"2023","unstructured":"Quan Yuan, Zhikun Zhang, Linkang Du, Min Chen, Peng Cheng, and Mingyang Sun. 2023. PrivGraph: Differentially Private Graph Data Publication by Exploiting Community Information. In 32nd USENIX Security Symposium, USENIX Security 2023. USENIX Association, 3241--3258."},{"key":"e_1_3_2_1_69_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.acl-long.74"},{"key":"e_1_3_2_1_70_1","doi-asserted-by":"publisher","DOI":"10.1145\/2588555.2588573"},{"key":"e_1_3_2_1_71_1","volume-title":"30th USENIX Security Symposium (USENIX Security 21)","author":"Zhang Zhikun","year":"2021","unstructured":"Zhikun Zhang, Tianhao Wang, Ninghui Li, Jean Honorio, Michael Backes, Shibo He, Jiming Chen, and Yang Zhang. 2021. {PrivSyn}: Differentially Private Data Synthesis. In 30th USENIX Security Symposium (USENIX Security 21). 929--946."}],"event":{"name":"IMC '24: ACM Internet Measurement Conference","sponsor":["SIGMETRICS ACM Special Interest Group on Measurement and Evaluation","SIGCOMM ACM Special Interest Group on Data Communication"],"location":"Madrid Spain","acronym":"IMC '24"},"container-title":["Proceedings of the 2024 ACM on Internet Measurement Conference"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3646547.3689011","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3646547.3689011","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T11:49:44Z","timestamp":1755863384000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3646547.3689011"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,4]]},"references-count":71,"alternative-id":["10.1145\/3646547.3689011","10.1145\/3646547"],"URL":"https:\/\/doi.org\/10.1145\/3646547.3689011","relation":{},"subject":[],"published":{"date-parts":[[2024,11,4]]},"assertion":[{"value":"2024-11-04","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}