{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:32:07Z","timestamp":1772119927586,"version":"3.50.1"},"reference-count":65,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,2,17]],"date-time":"2025-02-17T00:00:00Z","timestamp":1739750400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,2,17]],"date-time":"2025-02-17T00:00:00Z","timestamp":1739750400000},"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":["SIViP"],"published-print":{"date-parts":[[2025,4]]},"DOI":"10.1007\/s11760-025-03887-1","type":"journal-article","created":{"date-parts":[[2025,2,17]],"date-time":"2025-02-17T16:17:01Z","timestamp":1739809021000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Privacy-preserving non-negative matrix factorization for decentralized-data using correlated noise"],"prefix":"10.1007","volume":"19","author":[{"given":"Hafiz","family":"Imtiaz","sequence":"first","affiliation":[]},{"given":"Tusher","family":"Karmakar","sequence":"additional","affiliation":[]},{"given":"Protoye Kumar","family":"Mohanta","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,2,17]]},"reference":[{"key":"3887_CR1","first-page":"1457","volume":"5","author":"PO Hoyer","year":"2004","unstructured":"Hoyer, P.O.: Non-negative matrix factorization with sparseness constraints. J. Mach. Learn. Res. 5, 1457\u20131469 (2004)","journal-title":"J. Mach. Learn. Res."},{"key":"3887_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3632961","volume":"18","author":"S Saha","year":"2023","unstructured":"Saha, S., Imtiaz, H.: Privacy-preserving non-negative matrix factorization with outliers. ACM Trans. Knowl. Discov. Data 18, 1\u201326 (2023). https:\/\/doi.org\/10.1145\/3632961","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"3887_CR3","doi-asserted-by":"crossref","unstructured":"Dwork, C., McSherry, F., Nissim, K., Smith, A.: Calibrating noise to sensitivity in private data analysis. In: Theory of Cryptography Conference, pp. 265\u2013284 (2006). Springer","DOI":"10.1007\/11681878_14"},{"issue":"3\u20134","key":"3887_CR4","first-page":"211","volume":"9","author":"C Dwork","year":"2014","unstructured":"Dwork, C., Roth, A., et al.: The algorithmic foundations of differential privacy. Found. Trends Theor. Comput. Sci. 9(3\u20134), 211\u2013407 (2014)","journal-title":"Found. Trends Theor. Comput. Sci."},{"key":"3887_CR5","doi-asserted-by":"publisher","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. CCS \u201914, pp. 1054\u20131067. ACM, New York, NY, USA (2014). https:\/\/doi.org\/10.1145\/2660267.2660348","DOI":"10.1145\/2660267.2660348"},{"key":"3887_CR6","unstructured":"Apple: Learning with Privacy at Scale. Apple Machine Learning Journal (2017). https:\/\/machinelearning.apple.com\/2017\/12\/06\/learning-with-privacy-at-scale.html"},{"key":"3887_CR7","unstructured":"Bureau, U.C.: Protecting the Confidentiality of America\u2019s Statistics: Adopting Modern Disclosure Avoidance Methods at the Census Bureau. Census Blogs (2018). https:\/\/www.census.gov\/newsroom\/blogs\/research-matters\/2018\/08\/protecting_the_confi.html"},{"key":"3887_CR8","doi-asserted-by":"publisher","first-page":"6355","DOI":"10.1109\/TSP.2021.3126546","volume":"69","author":"H Imtiaz","year":"2021","unstructured":"Imtiaz, H., Mohammadi, J., Silva, R., Baker, B., Plis, S.M., Sarwate, A.D., Vince, C.D.: A correlated noise-assisted decentralized differentially private estimation protocol, and its application to fmri source separation. IEEE Trans. Sig. Proc. 69, 6355\u20136370 (2021). https:\/\/doi.org\/10.1109\/TSP.2021.3126546","journal-title":"IEEE Trans. Sig. Proc."},{"key":"3887_CR9","doi-asserted-by":"crossref","unstructured":"Fredrikson, M., Jha, S., Ristenpart, T.: Model inversion attacks that exploit confidence information and basic countermeasures. In: Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, pp. 1322\u20131333 (2015)","DOI":"10.1145\/2810103.2813677"},{"issue":"11s","key":"3887_CR10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3523273","volume":"54","author":"H Hu","year":"2022","unstructured":"Hu, H., Salcic, Z., Sun, L., Dobbie, G., Yu, P.S., Zhang, X.: Membership inference attacks on machine learning: a survey. ACM Comput. Surv. (CSUR) 54(11s), 1\u201337 (2022)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"3887_CR11","doi-asserted-by":"crossref","unstructured":"Shokri, R., Stronati, M., Song, C., Shmatikov, V.: Membership inference attacks against machine learning models. In: 2017 IEEE Symposium on Security and Privacy (SP), pp. 3\u201318 (2017). IEEE","DOI":"10.1109\/SP.2017.41"},{"key":"3887_CR12","unstructured":"Narayanan, A., Shmatikov, V.: How to break anonymity of the netflix prize dataset. arXiv:cs\/0610105 (2006)"},{"issue":"2","key":"3887_CR13","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1109\/TAC.2013.2283096","volume":"59","author":"J Le Ny","year":"2013","unstructured":"Le Ny, J., Pappas, G.J.: Differentially private filtering. IEEE Trans. Autom. Control 59(2), 341\u2013354 (2013)","journal-title":"IEEE Trans. Autom. Control"},{"issue":"9","key":"3887_CR14","first-page":"29","volume":"2015092903","author":"L Sweeney","year":"2015","unstructured":"Sweeney, L.: Only you, your doctor, and many others may know. Technol. Sci. 2015092903(9), 29 (2015)","journal-title":"Technol. Sci."},{"key":"3887_CR15","unstructured":"Zhu, L., Liu, Z., Han, S.: Deep leakage from gradients. Adv. Neural Inform. Process. Syst. 32 (2019)"},{"key":"3887_CR16","doi-asserted-by":"crossref","unstructured":"Mironov, I.: R\u00e9nyi differential privacy. In: 2017 IEEE 30th Computer Security Foundations Symposium (CSF), pp. 263\u2013275 (2017). IEEE","DOI":"10.1109\/CSF.2017.11"},{"key":"3887_CR17","unstructured":"Lee, D., Seung, H.S.: Algorithms for non-negative matrix factorization. Adv. Neural Inform. Process. Syst. 13 (2000)"},{"issue":"2","key":"3887_CR18","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1007\/s11464-012-0194-5","volume":"7","author":"Y Xu","year":"2012","unstructured":"Xu, Y., Yin, W., Wen, Z., Zhang, Y.: An alternating direction algorithm for matrix completion with nonnegative factors. Front. Math. China 7(2), 365\u2013384 (2012)","journal-title":"Front. Math. China"},{"key":"3887_CR19","doi-asserted-by":"crossref","unstructured":"Kim, J., Park, H.: Toward faster nonnegative matrix factorization: a new algorithm and comparisons. In: 2008 Eighth IEEE International Conference on Data Mining, pp. 353\u2013362 (2008). IEEE","DOI":"10.1109\/ICDM.2008.149"},{"issue":"2","key":"3887_CR20","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1137\/07069239X","volume":"30","author":"H Kim","year":"2008","unstructured":"Kim, H., Park, H.: Nonnegative matrix factorization based on alternating nonnegativity constrained least squares and active set method. SIAM J. Matrix Anal. Appl. 30(2), 713\u2013730 (2008)","journal-title":"SIAM J. Matrix Anal. Appl."},{"issue":"10","key":"3887_CR21","doi-asserted-by":"crossref","first-page":"2756","DOI":"10.1162\/neco.2007.19.10.2756","volume":"19","author":"C-J Lin","year":"2007","unstructured":"Lin, C.-J.: Projected gradient methods for nonnegative matrix factorization. Neural Comput. 19(10), 2756\u20132779 (2007)","journal-title":"Neural Comput."},{"issue":"3","key":"3887_CR22","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1109\/TSP.2016.2620967","volume":"65","author":"R Zhao","year":"2016","unstructured":"Zhao, R., Tan, V.Y.: Online nonnegative matrix factorization with outliers. IEEE Trans. Sig. Process. 65(3), 555\u2013570 (2016)","journal-title":"IEEE Trans. Sig. Process."},{"issue":"6","key":"3887_CR23","doi-asserted-by":"crossref","first-page":"1336","DOI":"10.1109\/TKDE.2012.51","volume":"25","author":"Y-X Wang","year":"2012","unstructured":"Wang, Y.-X., Zhang, Y.-J.: Nonnegative matrix factorization: a comprehensive review. IEEE Trans. Knowl. Data Eng. 25(6), 1336\u20131353 (2012)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"2","key":"3887_CR24","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1007\/s10898-013-0035-4","volume":"58","author":"J Kim","year":"2014","unstructured":"Kim, J., He, Y., Park, H.: Algorithms for nonnegative matrix and tensor factorizations: a unified view based on block coordinate descent framework. J. Glob. Optim. 58(2), 285\u2013319 (2014)","journal-title":"J. Glob. Optim."},{"key":"3887_CR25","doi-asserted-by":"crossref","unstructured":"Song, S., Chaudhuri, K., Sarwate, A.D.: Stochastic gradient descent with differentially private updates. In: 2013 IEEE Global Conference on Signal and Information Processing, pp. 245\u2013248 (2013). IEEE","DOI":"10.1109\/GlobalSIP.2013.6736861"},{"key":"3887_CR26","doi-asserted-by":"crossref","unstructured":"Bassily, R., Smith, A., Thakurta, A.: Private empirical risk minimization: Efficient algorithms and tight error bounds. In: 2014 IEEE 55th Annual Symposium on Foundations of Computer Science, pp. 464\u2013473 (2014). IEEE","DOI":"10.1109\/FOCS.2014.56"},{"issue":"3","key":"3887_CR27","first-page":"1069","volume":"12","author":"K Chaudhuri","year":"2011","unstructured":"Chaudhuri, K., Monteleoni, C., Sarwate, A.D.: Differentially private empirical risk minimization. J. Mach. Learn. Res. 12(3), 1069\u20131109 (2011)","journal-title":"J. Mach. Learn. Res."},{"issue":"1","key":"3887_CR28","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1109\/TCNS.2016.2614100","volume":"5","author":"E Nozari","year":"2016","unstructured":"Nozari, E., Tallapragada, P., Cort\u00e9s, J.: Differentially private distributed convex optimization via functional perturbation. IEEE Trans. Control Netw. Syst. 5(1), 395\u2013408 (2016)","journal-title":"IEEE Trans. Control Netw. Syst."},{"key":"3887_CR29","doi-asserted-by":"crossref","unstructured":"McSherry, F., Talwar, K.: Mechanism design via differential privacy. In: 48th Annual IEEE Symposium on Foundations of Computer Science (FOCS\u201907), pp. 94\u2013103 (2007). IEEE","DOI":"10.1109\/FOCS.2007.66"},{"key":"3887_CR30","doi-asserted-by":"crossref","unstructured":"Nissim, K., Raskhodnikova, S., Smith, A.: Smooth sensitivity and sampling in private data analysis. In: Proceedings of the Thirty-ninth Annual ACM Symposium on Theory of Computing, pp. 75\u201384 (2007)","DOI":"10.1145\/1250790.1250803"},{"key":"3887_CR31","doi-asserted-by":"crossref","unstructured":"Lari, E., Arablouei, R., Werner, S.: Privacy-preserving distributed nonnegative matrix factorization. arXiv preprint arXiv:2403.18326 (2024)","DOI":"10.23919\/EUSIPCO63174.2024.10715358"},{"issue":"2","key":"3887_CR32","doi-asserted-by":"crossref","first-page":"653","DOI":"10.1109\/TKDE.2020.2985964","volume":"34","author":"Y Qian","year":"2020","unstructured":"Qian, Y., Tan, C., Ding, D., Li, H., Mamoulis, N.: Fast and secure distributed nonnegative matrix factorization. IEEE Trans. Knowl. Data Eng. 34(2), 653\u2013666 (2020)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"3887_CR33","doi-asserted-by":"crossref","unstructured":"Wei, H., Ge, L., Lu, Z., Zhang, G., Qin, D.: Differential privacy image publishing based on nmf and svd. In: Proceedings of the 2021 13th International Conference on Bioinformatics and Biomedical Technology, pp. 48\u201354 (2021)","DOI":"10.1145\/3473258.3473266"},{"issue":"1","key":"3887_CR34","first-page":"1","volume":"3","author":"S Boyd","year":"2011","unstructured":"Boyd, S., Parikh, N., Chu, E., Peleato, B., Eckstein, J., et al.: Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends\u00ae Mach. Learn. 3(1), 1\u2013122 (2011)","journal-title":"Found. Trends\u00ae Mach. Learn."},{"issue":"6","key":"3887_CR35","doi-asserted-by":"crossref","first-page":"2941","DOI":"10.1109\/TSG.2017.2720471","volume":"8","author":"DK Molzahn","year":"2017","unstructured":"Molzahn, D.K., D\u00f6rfler, F., Sandberg, H., Low, S.H., Chakrabarti, S., Baldick, R., Lavaei, J.: A survey of distributed optimization and control algorithms for electric power systems. IEEE Trans. Smart Grid 8(6), 2941\u20132962 (2017)","journal-title":"IEEE Trans. Smart Grid"},{"key":"3887_CR36","unstructured":"Uribe, C.A., Lee, S., Gasnikov, A., Nedi\u0107, A.: Optimal algorithms for distributed optimization. arXiv preprint arXiv:1712.00232 (2017)"},{"issue":"1","key":"3887_CR37","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1109\/TAC.2008.2009515","volume":"54","author":"A Nedic","year":"2009","unstructured":"Nedic, A., Ozdaglar, A.: Distributed subgradient methods for multi-agent optimization. IEEE Trans. Autom. Control 54(1), 48\u201361 (2009)","journal-title":"IEEE Trans. Autom. Control"},{"issue":"9","key":"3887_CR38","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1109\/TAC.1986.1104412","volume":"31","author":"J Tsitsiklis","year":"1986","unstructured":"Tsitsiklis, J., Bertsekas, D., Athans, M.: Distributed asynchronous deterministic and stochastic gradient optimization algorithms. IEEE Trans. Autom. Control 31(9), 803\u2013812 (1986)","journal-title":"IEEE Trans. Autom. Control"},{"issue":"1","key":"3887_CR39","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1109\/TAC.2016.2541298","volume":"62","author":"S Han","year":"2016","unstructured":"Han, S., Topcu, U., Pappas, G.J.: Differentially private distributed constrained optimization. IEEE Trans. Autom. Control 62(1), 50\u201364 (2016)","journal-title":"IEEE Trans. Autom. Control"},{"key":"3887_CR40","doi-asserted-by":"crossref","unstructured":"Nozari, E., Tallapragada, P., Cort\u00e9s, J.: Differentially private distributed convex optimization via objective perturbation. In: 2016 American Control Conference (ACC), pp. 2061\u20132066 (2016). IEEE","DOI":"10.1109\/ACC.2016.7525222"},{"issue":"1","key":"3887_CR41","first-page":"4","volume":"4","author":"J Zhu","year":"2018","unstructured":"Zhu, J., Xu, C., Guan, J., Wu, D.O.: Differentially private distributed online algorithms over time-varying directed networks. IEEE Trans. Sig. Inform. Process. Over Netw. 4(1), 4\u201317 (2018)","journal-title":"IEEE Trans. Sig. Inform. Process. Over Netw."},{"key":"3887_CR42","doi-asserted-by":"crossref","unstructured":"Song, S., Chaudhuri, K., Sarwate, A.D.: Stochastic gradient descent with differentially private updates. In: 2013 IEEE Global Conference on Signal and Information Processing, pp. 245\u2013248 (2013). IEEE","DOI":"10.1109\/GlobalSIP.2013.6736861"},{"key":"3887_CR43","doi-asserted-by":"crossref","unstructured":"Bassily, R., Smith, A., Thakurta, A.: Private empirical risk minimization: Efficient algorithms and tight error bounds. In: 2014 IEEE 55th Annual Symposium on Foundations of Computer Science, pp. 464\u2013473 (2014). IEEE","DOI":"10.1109\/FOCS.2014.56"},{"issue":"3","key":"3887_CR44","first-page":"1069","volume":"12","author":"K Chaudhuri","year":"2011","unstructured":"Chaudhuri, K., Monteleoni, C., Sarwate, A.D.: Differentially private empirical risk minimization. J. Mach. Learn. Res. 12(3), 1069\u20131109 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"3887_CR45","doi-asserted-by":"crossref","unstructured":"Smith, A.: Privacy-preserving statistical estimation with optimal convergence rates. In: Proceedings of the Forty-third Annual ACM Symposium on Theory of Computing, pp. 813\u2013822 (2011)","DOI":"10.1145\/1993636.1993743"},{"key":"3887_CR46","doi-asserted-by":"crossref","unstructured":"McSherry, F., Talwar, K.: Mechanism design via differential privacy. In: 48th Annual IEEE Symposium on Foundations of Computer Science (FOCS\u201907), pp. 94\u2013103 (2007). IEEE","DOI":"10.1109\/FOCS.2007.66"},{"key":"3887_CR47","doi-asserted-by":"publisher","first-page":"1466","DOI":"10.1109\/TIFS.2022.3163591","volume":"17","author":"D Ye","year":"2022","unstructured":"Ye, D., Shen, S., Zhu, T., Liu, B., Zhou, W.: One parameter defense-defending against data inference attacks via differential privacy. IEEE Trans. Inform. Forensics Secur. 17, 1466\u20131480 (2022). https:\/\/doi.org\/10.1109\/TIFS.2022.3163591","journal-title":"IEEE Trans. Inform. Forensics Secur."},{"key":"3887_CR48","doi-asserted-by":"crossref","unstructured":"Ha, T., Vo, T., Dang, T.K., Trang, N.T.H.: Differential privacy under membership inference attacks. In: International Conference on Future Data and Security Engineering, pp. 255\u2013269 (2023). Springer","DOI":"10.1007\/978-981-99-8296-7_18"},{"key":"3887_CR49","doi-asserted-by":"publisher","unstructured":"Cheng, Z., Li, Z., Zhang, J., Zhang, S.: Differentially private machine learning model against model extraction attack. In: 2020 International Conferences on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics), pp. 722\u2013728 (2020). https:\/\/doi.org\/10.1109\/iThings-GreenCom-CPSCom-SmartData-Cybermatics50389.2020.00125","DOI":"10.1109\/iThings-GreenCom-CPSCom-SmartData-Cybermatics50389.2020.00125"},{"issue":"5","key":"3887_CR50","doi-asserted-by":"publisher","first-page":"825","DOI":"10.3390\/e25050825","volume":"25","author":"N Tasnim","year":"2023","unstructured":"Tasnim, N., Mohammadi, J., Sarwate, A.D., Imtiaz, H.: Approximating functions with approximate privacy for applications in signal estimation and learning. Entropy 25(5), 825 (2023). https:\/\/doi.org\/10.3390\/e25050825","journal-title":"Entropy"},{"key":"3887_CR51","unstructured":"Wang, Y.-X., Balle, B., Kasiviswanathan, S.P.: Subsampled renyi differential privacy and analytical moments accountant. In: Chaudhuri, K., Sugiyama, M. (eds.) Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research, vol. 89, pp. 1226\u20131235. PMLR (2019). https:\/\/proceedings.mlr.press\/v89\/wang19b.html"},{"key":"3887_CR52","doi-asserted-by":"publisher","unstructured":"Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H.B., Patel, S., Ramage, D., Segal, A., Seth, K.: Practical secure aggregation for privacy-preserving machine learning. In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. CCS \u201917, pp. 1175\u20131191. ACM, New York, NY, USA (2017). https:\/\/doi.org\/10.1145\/3133956.3133982","DOI":"10.1145\/3133956.3133982"},{"key":"3887_CR53","doi-asserted-by":"crossref","unstructured":"Abadi, M., Chu, A., Goodfellow, I., McMahan, H.B., Mironov, I., Talwar, K., Zhang, L.: Deep learning with differential privacy. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, pp. 308\u2013318 (2016)","DOI":"10.1145\/2976749.2978318"},{"key":"3887_CR54","doi-asserted-by":"crossref","unstructured":"Bottou, L.: On-line Learning and Stochastic Approximations. In: Saad, D. (ed.) On-line Learning in Neural Networks, pp. 9\u201342. Cambridge University Press, New York, NY, USA (1998). http:\/\/dl.acm.org\/citation.cfm?id=304710.304720","DOI":"10.1017\/CBO9780511569920.003"},{"key":"3887_CR55","first-page":"17455","volume":"34","author":"G Andrew","year":"2021","unstructured":"Andrew, G., Thakkar, O., McMahan, B., Ramaswamy, S.: Differentially private learning with adaptive clipping. Adv. Neural Inform. Process. Syst. 34, 17455\u201317466 (2021)","journal-title":"Adv. Neural Inform. Process. Syst."},{"issue":"4","key":"3887_CR56","doi-asserted-by":"crossref","first-page":"1350","DOI":"10.1016\/j.patcog.2007.09.010","volume":"41","author":"C Boutsidis","year":"2008","unstructured":"Boutsidis, C., Gallopoulos, E.: Svd based initialization: a head start for nonnegative matrix factorization. Pattern Recognit. 41(4), 1350\u20131362 (2008)","journal-title":"Pattern Recognit."},{"key":"3887_CR57","first-page":"2905","volume":"14","author":"K Chaudhuri","year":"2013","unstructured":"Chaudhuri, K., Sarwate, A.D., Sinha, K.: A near-optimal algorithm for differentially-private principal components. J. Mach. Learn. Res. 14, 2905\u20132943 (2013)","journal-title":"J. Mach. Learn. Res."},{"issue":"13","key":"3887_CR58","doi-asserted-by":"crossref","first-page":"5645","DOI":"10.1016\/j.eswa.2015.02.055","volume":"42","author":"D O\u2019callaghan","year":"2015","unstructured":"O\u2019callaghan, D., Greene, D., Carthy, J., Cunningham, P.: An analysis of the coherence of descriptors in topic modeling. Exp. Syst. Appl. 42(13), 5645\u20135657 (2015)","journal-title":"Exp. Syst. Appl."},{"key":"3887_CR59","unstructured":"Dua, D., Graff, C.: UCI Machine Learning Repository (2017). http:\/\/archive.ics.uci.edu\/ml"},{"key":"3887_CR60","first-page":"361","volume":"5","author":"DD Lewis","year":"2004","unstructured":"Lewis, D.D., Yang, Y., Rose, T.G., Li, F.: Rcv1: a new benchmark collection for text categorization research. J. Mach. Learn. Res. 5, 361\u2013397 (2004)","journal-title":"J. Mach. Learn. Res."},{"key":"3887_CR61","unstructured":"Cieri, C., Graff, D., Liberman, M., Martey, N., Strassel, S., et al.: The tdt-2 text and speech corpus. In: Proceedings of the DARPA Broadcast News Workshop, pp. 57\u201360 (1999)"},{"key":"3887_CR62","unstructured":"Yale, A.: The extended yale face database B (2001)"},{"key":"3887_CR63","doi-asserted-by":"crossref","unstructured":"Weyrauch, B., Heisele, B., Huang, J., Blanz, V.: Component-based face recognition with 3d morphable models. In: Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW). vol. 5, p. 85. IEEE Computer Society, Washington, DC, USA (2004). http:\/\/dl.acm.org\/citation.cfm?id=1032636.1032976","DOI":"10.1109\/CVPR.2004.315"},{"key":"3887_CR64","doi-asserted-by":"crossref","unstructured":"Geng, Q., Kairouz, P., Oh, S., Viswanath, P.: The staircase mechanism in differential privacy. IEEE J. Sel. Top. Sig. Process. 9, 1176\u20131184 (2015)","DOI":"10.1109\/JSTSP.2015.2425831"},{"key":"3887_CR65","unstructured":"Near, J.P., Abuah, C.: Programming Differential Privacy vol. 1, (2021). https:\/\/uvm-plaid.github.io\/programming-dp\/"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-025-03887-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-025-03887-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-025-03887-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,3]],"date-time":"2025-04-03T03:30:44Z","timestamp":1743651044000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-025-03887-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,17]]},"references-count":65,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,4]]}},"alternative-id":["3887"],"URL":"https:\/\/doi.org\/10.1007\/s11760-025-03887-1","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-5044244\/v1","asserted-by":"object"}]},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,17]]},"assertion":[{"value":"6 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 November 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 January 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 February 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"298"}}