{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T06:12:49Z","timestamp":1770271969583,"version":"3.49.0"},"reference-count":48,"publisher":"Association for Computing Machinery (ACM)","issue":"11","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2025,7]]},"abstract":"<jats:p>\n            Although a large amount of valuable knowledge can be obtained from the weighted graph snapshots modeled over time, it may cause privacy issues. Local differential privacy (LDP) provides a strong solution for private graph data publishing in decentralized networks. However, most existing LDP studies over graphs are only applicable to static unweighted graphs. This paper investigates the problem of continuous publication of weighted graph snapshots and proposes a graph publication framework, WGT-LDP, under\n            <jats:italic toggle=\"yes\">w<\/jats:italic>\n            -event edge weight LDP, which can protect the privacy of edges and weights over any\n            <jats:italic toggle=\"yes\">w<\/jats:italic>\n            consecutive time steps. WGT-LDP consists of four key components: population division-based sampling that overcomes the problem of over-segmentation of the privacy budget, data range estimation that mitigates noise on edge weights, aggregate information collection that obtains important information about the graph structure and edge weights, and graph snapshot generation that reconstructs weighted graph snapshot at each time step. We provide theoretical guarantees on privacy and utility, and perform extensive experiments on three real-world and two synthetic datasets, using four commonly used metrics. Our experiments show that WGT-LDP produces high-quality synthetic weighted graphs and significantly outperforms baseline methods.\n          <\/jats:p>","DOI":"10.14778\/3749646.3749688","type":"journal-article","created":{"date-parts":[[2025,9,4]],"date-time":"2025-09-04T17:55:06Z","timestamp":1757008506000},"page":"4214-4226","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Continuous Publication of Weighted Graphs with Local Differential Privacy"],"prefix":"10.14778","volume":"18","author":[{"given":"Wen","family":"Xu","sequence":"first","affiliation":[{"name":"Jinan University"}]},{"given":"Pengpeng","family":"Qiao","sequence":"additional","affiliation":[{"name":"Institute of Science Tokyo"}]},{"given":"Shang","family":"Liu","sequence":"additional","affiliation":[{"name":"China University of Mining and Technology"}]},{"given":"Zhirun","family":"Zheng","sequence":"additional","affiliation":[{"name":"Ajou University"}]},{"given":"Yang","family":"Cao","sequence":"additional","affiliation":[{"name":"Institute of Science Tokyo"}]},{"given":"Zhetao","family":"Li","sequence":"additional","affiliation":[{"name":"Jinan University"}]}],"member":"320","published-online":{"date-parts":[[2025,9,4]]},"reference":[{"key":"e_1_2_1_1_1","first-page":"2258","article-title":"CGM: an enhanced mechanism for streaming data collection with local differential privacy","volume":"14","author":"Bao Ergute","year":"2021","unstructured":"Ergute Bao, Yin Yang, Xiaokui Xiao, and Bolin Ding. 2021. CGM: an enhanced mechanism for streaming data collection with local differential privacy. VLDB 14, 11 (2021), 2258\u20132270.","journal-title":"VLDB"},{"key":"e_1_2_1_2_1","first-page":"1","article-title":"Global and local differentially private release of count-weighted graphs","volume":"1","author":"Brito Felipe T","year":"2023","unstructured":"Felipe T Brito, Victor AE Farias, Cheryl Flynn, Subhabrata Majumdar, Javam C Machado, and Divesh Srivastava. 2023. Global and local differentially private release of count-weighted graphs. SIGMOD 1, 2 (2023), 1\u201325.","journal-title":"SIGMOD"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-013-0344-8"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1557019.1557047"},{"key":"e_1_2_1_5_1","volume-title":"Efficient immunization strategies for computer networks and populations. Physical review letters 91, 24","author":"Cohen Reuven","year":"2003","unstructured":"Reuven Cohen, Shlomo Havlin, and Daniel Ben-Avraham. 2003. Efficient immunization strategies for computer networks and populations. Physical review letters 91, 24 (2003), 247901."},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/502512.502525"},{"key":"e_1_2_1_7_1","volume-title":"Local privacy and statistical minimax rates. In 2013 IEEE 54th annual symposium on foundations of computer science","author":"Duchi John C","unstructured":"John C Duchi, Michael I Jordan, and Martin J Wainwright. 2013. Local privacy and statistical minimax rates. In 2013 IEEE 54th annual symposium on foundations of computer science. IEEE, 429\u2013438."},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/11681878_14"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/11681878_14"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.epidem.2008.12.001"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611975482.151"},{"key":"e_1_2_1_12_1","volume-title":"Madhav V Marathe, Aravind Srinivasan, Zoltan Toroczkai, and Nan Wang.","author":"Eubank Stephen","year":"2004","unstructured":"Stephen Eubank, Hasan Guclu, VS Anil Kumar, Madhav V Marathe, Aravind Srinivasan, Zoltan Toroczkai, and Nan Wang. 2004. Modelling disease outbreaks in realistic urban social networks. Nature 429, 6988 (2004), 180\u2013184."},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/1536414.1536464"},{"key":"e_1_2_1_14_1","unstructured":"Aric Hagberg Pieter J Swart and Daniel A Schult. 2008. Exploring network structure dynamics and function using NetworkX. Technical Report. Los Alamos National Laboratory (LANL) Los Alamos NM (United States)."},{"key":"e_1_2_1_15_1","volume-title":"Workshop on Social Network Mining & Analysis, ACM KDD","volume":"130","author":"Hangal Sudheendra","year":"2010","unstructured":"Sudheendra Hangal, Diana MacLean, Monica S Lam, and Jeffrey Heer. 2010. All friends are not equal: Using weights in social graphs to improve search. In Workshop on Social Network Mining & Analysis, ACM KDD, Vol. 130."},{"key":"e_1_2_1_16_1","volume-title":"Real-Time Trajectory Synthesis with Local Differential Privacy. arXiv preprint arXiv:2404.11450","author":"Hu Yujia","year":"2024","unstructured":"Yujia Hu, Yuntao Du, Zhikun Zhang, Ziquan Fang, Lu Chen, Kai Zheng, and Yunjun Gao. 2024. Real-Time Trajectory Synthesis with Local Differential Privacy. arXiv preprint arXiv:2404.11450 (2024)."},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3128946"},{"key":"e_1_2_1_18_1","doi-asserted-by":"crossref","unstructured":"Zach Jorgensen Ting Yu and Graham Cormode. 2016. Publishing attributed social graphs with formal privacy guarantees. In SIGMOD. 107\u2013122.","DOI":"10.1145\/2882903.2915215"},{"key":"e_1_2_1_19_1","volume-title":"Local differential privacy for evolving data. NeuIPS 31","author":"Joseph Matthew","year":"2018","unstructured":"Matthew Joseph, Aaron Roth, Jonathan Ullman, and Bo Waggoner. 2018. Local differential privacy for evolving data. NeuIPS 31 (2018)."},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1137\/090756090"},{"key":"e_1_2_1_21_1","doi-asserted-by":"crossref","unstructured":"Georgios Kellaris Stavros Papadopoulos Xiaokui Xiao and Dimitris Papadias. 2014. Differentially private event sequences over infinite streams. (2014).","DOI":"10.14778\/2732977.2732989"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/956750.956769"},{"key":"e_1_2_1_23_1","volume-title":"Information theory and statistics","author":"Kullback Solomon","unstructured":"Solomon Kullback. 1997. Information theory and statistics. Courier Corporation."},{"key":"e_1_2_1_24_1","first-page":"4453","article-title":"Space-Efficient Subgraph Search Over Streaming Graph With Timing Order Constraint","volume":"34","author":"Li Youhuan","year":"2020","unstructured":"Youhuan Li, Lei Zou, M Tamer \u00d6zsu, and Dongyan Zhao. 2020. Space-Efficient Subgraph Search Over Streaming Graph With Timing Order Constraint. TKDE 34, 9 (2020), 4453\u20134467.","journal-title":"TKDE"},{"key":"e_1_2_1_25_1","doi-asserted-by":"crossref","unstructured":"Zitao Li Tianhao Wang Milan Lopuha\u00e4-Zwakenberg Ninghui Li and Boris \u0160koric. 2020. Estimating numerical distributions under local differential privacy. In SIGMOD. 621\u2013635.","DOI":"10.1145\/3318464.3389700"},{"key":"e_1_2_1_26_1","volume-title":"User-Driven Privacy-Preserving Data Streams Release for Multi-Task Assignment in Mobile Crowdsensing. TMC","author":"Li Zhetao","year":"2024","unstructured":"Zhetao Li, Junru Wu, Saiqin Long, Zhirun Zheng, Chengxin Li, and Mianxiong Dong. 2024. User-Driven Privacy-Preserving Data Streams Release for Multi-Task Assignment in Mobile Crowdsensing. TMC (2024)."},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2025.3543605"},{"key":"e_1_2_1_28_1","volume-title":"Pattern-sensitive Local Differential Privacy for Finite-Range Time-series Data in Mobile Crowdsensing. TMC","author":"Li Zhetao","year":"2024","unstructured":"Zhetao Li, Xiyu Zeng, Yong Xiao, Chengxin Li, Wentai Wu, and Haolin Liu. 2024. Pattern-sensitive Local Differential Privacy for Finite-Range Time-series Data in Mobile Crowdsensing. TMC (2024)."},{"key":"e_1_2_1_29_1","doi-asserted-by":"crossref","unstructured":"Frank D McSherry. 2009. Privacy integrated queries: an extensible platform for privacy-preserving data analysis. In SIGMOD. 19\u201330.","DOI":"10.1145\/1559845.1559850"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.93.098701"},{"key":"e_1_2_1_31_1","volume-title":"Triadic closure in two-mode networks: Redefining the global and local clustering coefficients. Social networks 35, 2","author":"Opsahl Tore","year":"2013","unstructured":"Tore Opsahl. 2013. Triadic closure in two-mode networks: Redefining the global and local clustering coefficients. Social networks 35, 2 (2013), 159\u2013167."},{"key":"e_1_2_1_32_1","volume-title":"Austin R Benson, and Jure Leskovec","author":"Paranjape Ashwin","year":"2017","unstructured":"Ashwin Paranjape, Austin R Benson, and Jure Leskovec. 2017. Motifs in temporal networks. In WSDM. 601\u2013610."},{"key":"e_1_2_1_33_1","doi-asserted-by":"crossref","unstructured":"Zhan Qin Ting Yu Yin Yang Issa Khalil Xiaokui Xiao and Kui Ren. 2017. Generating synthetic decentralized social graphs with local differential privacy. In CCS. 425\u2013438.","DOI":"10.1145\/3133956.3134086"},{"key":"e_1_2_1_34_1","doi-asserted-by":"crossref","unstructured":"Xuebin Ren Liang Shi Weiren Yu Shusen Yang Cong Zhao and Zongben Xu. 2022. LDP-IDS: Local differential privacy for infinite data streams. In SIGMOD. 1064\u20131077.","DOI":"10.1145\/3514221.3526190"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v29i1.9277"},{"key":"e_1_2_1_36_1","volume-title":"Data breaches increase 40%","author":"Seals Tara","year":"2016","unstructured":"Tara Seals. 2017. Data breaches increase 40% in 2016. https:\/\/www.infosecurity-magazine.com\/news\/data-breaches-increase-40-in-2016\/."},{"key":"e_1_2_1_37_1","volume-title":"Community structure and scale-free collections of Erd\u0151s-R\u00e9nyi graphs. Physical Review E\u2014Statistical, Nonlinear, and Soft Matter Physics 85, 5","author":"Seshadhri Comandur","year":"2012","unstructured":"Comandur Seshadhri, Tamara G Kolda, and Ali Pinar. 2012. Community structure and scale-free collections of Erd\u0151s-R\u00e9nyi graphs. Physical Review E\u2014Statistical, Nonlinear, and Soft Matter Physics 85, 5 (2012), 056109."},{"key":"e_1_2_1_38_1","volume-title":"2024 IEEE Symposium on Security and Privacy (SP). IEEE Computer Society, 237\u2013237","author":"Wagaman Connor","year":"2024","unstructured":"Connor Wagaman, Palak Jain, and Adam Smith. 2024. Time-Aware Projections: Truly Node-Private Graph Statistics under Continual Observation. In 2024 IEEE Symposium on Security and Privacy (SP). IEEE Computer Society, 237\u2013237."},{"key":"e_1_2_1_39_1","volume-title":"Zhikun Zhang, Dong Su, Yueqiang Cheng, Zhou Li, Ninghui Li, and Somesh Jha.","author":"Wang Tianhao","year":"2021","unstructured":"Tianhao Wang, Joann Qiongna Chen, Zhikun Zhang, Dong Su, Yueqiang Cheng, Zhou Li, Ninghui Li, and Somesh Jha. 2021. Continuous release of data streams under both centralized and local differential privacy. In CCS. 1237\u20131253."},{"key":"e_1_2_1_40_1","volume-title":"Locally differentially private frequency estimation with consistency. arXiv preprint arXiv:1905.08320","author":"Wang Tianhao","year":"2019","unstructured":"Tianhao Wang, Milan Lopuha\u00e4-Zwakenberg, Zitao Li, Boris Skoric, and Ninghui Li. 2019. Locally differentially private frequency estimation with consistency. arXiv preprint arXiv:1905.08320 (2019)."},{"key":"e_1_2_1_41_1","volume-title":"Towards pattern-aware privacy-preserving real-time data collection","author":"Wang Zhibo","unstructured":"Zhibo Wang, Wenxin Liu, Xiaoyi Pang, Ju Ren, Zhe Liu, and Yongle Chen. 2020. Towards pattern-aware privacy-preserving real-time data collection. In INFOCOM. IEEE, 109\u2013118."},{"key":"e_1_2_1_42_1","first-page":"3239","article-title":"AsgLDP: Collecting and generating decentralized attributed graphs with local differential privacy","volume":"15","author":"Wei Chengkun","year":"2020","unstructured":"Chengkun Wei, Shouling Ji, Changchang Liu, Wenzhi Chen, and Ting Wang. 2020. AsgLDP: Collecting and generating decentralized attributed graphs with local differential privacy. TIFS 15 (2020), 3239\u20133254.","journal-title":"TIFS"},{"key":"e_1_2_1_43_1","doi-asserted-by":"crossref","unstructured":"Qian Xiao Rui Chen and Kian-Lee Tan. 2014. Differentially private network data release via structural inference. In SIGKDD. 911\u2013920.","DOI":"10.1145\/2623330.2623642"},{"key":"e_1_2_1_44_1","volume-title":"Differentially Private Weighted Graphs Publication Under Continuous Monitoring. TMC","author":"Xu Wen","year":"2024","unstructured":"Wen Xu, Zhetao Li, Haolin Liu, Yunjun Gao, Xiaofei Liao, and Kenli Li. 2024. Differentially Private Weighted Graphs Publication Under Continuous Monitoring. TMC (2024)."},{"key":"e_1_2_1_45_1","unstructured":"Wen Xu Pengpeng Qiao Shang Liu Zhirun Zheng Yang Cao and Zhetao Li. 2025. Technical Report. https:\/\/github.com\/xuwen22\/WGT-LDP\/blob\/main\/technical_report.pdf."},{"key":"e_1_2_1_46_1","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 USENIX Security. 3241\u20133258."},{"key":"e_1_2_1_47_1","volume-title":"PSGraph: Differentially Private Streaming Graph Synthesis by Considering Temporal Dynamics. arXiv preprint arXiv:2412.11369","author":"Yuan Quan","year":"2024","unstructured":"Quan Yuan, Zhikun Zhang, Linkang Du, Min Chen, Mingyang Sun, Yunjun Gao, Michael Backes, Shibo He, and Jiming Chen. 2024. PSGraph: Differentially Private Streaming Graph Synthesis by Considering Temporal Dynamics. arXiv preprint arXiv:2412.11369 (2024)."},{"key":"e_1_2_1_48_1","volume-title":"Vincent YF Tan, and Xiaokui Xiao","author":"Zhu Xiaochen","year":"2023","unstructured":"Xiaochen Zhu, Vincent YF Tan, and Xiaokui Xiao. 2023. Blink: Link Local Differential Privacy in Graph Neural Networks via Bayesian Estimation. In CCS. 2651\u20132664."}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3749646.3749688","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,5]],"date-time":"2025-09-05T03:24:40Z","timestamp":1757042680000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3749646.3749688"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7]]},"references-count":48,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2025,7]]}},"alternative-id":["10.14778\/3749646.3749688"],"URL":"https:\/\/doi.org\/10.14778\/3749646.3749688","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2025,7]]},"assertion":[{"value":"2025-09-04","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}