Skip to main content
SpringerLink
Log in

Blockchain transaction deanonymization using ensemble learning

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Bitcoin is a digital currency that provides a way to transact without any trusted intermediary; however, privacy is an issue. Numerous deanonymization endeavors have been proposed, in spite of the fact that Bitcoin addresses aren’t linked with a specific identity. In this work, blockchain transactions are deanonymized using ensemble learning. An excess of four million labeled dataset samples comprising user activities such as pools, services, gambling, and exchanges have been gathered from various repositories and prepared for training and validation to perform the classification. The main aim is to deanonymize blockchain transactions via classification and separate legitimate ones from illegitimate ones. On the class imbalanced dataset, remarkable cross-validation accuracy was attained using the EXtreme Gradient Boosting with default parameters and hyperparameters. Using EXtreme Gradient Boosting, Random Forest, and Bagging on the class-balanced dataset produced the best cross-validation accuracy when using the default parameters and hyperparameters. The empirical findings indicate that the effectiveness of the proposed deanonymization using the proposed ensemble learning model has achieved up to 98.45% accuracy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Data availability

The data set generated and/or analyzed during the current study is available upon reasonable request from the corresponding author. However, data sets are available as open source.

References

  1. Nayyer N, Javaid N, Akbar Ma, Aldegheıshem A, Alrajeh N, Jamil M (2023) A new framework for fraud detection in Bitcoin transactions through Ensemble Stacking Model in Smart cities. IEEE Access 11:90916–90938. https://doi.org/10.1109/ACCESS.2023.3308298

    Article  Google Scholar 

  2. Mundhe P, Phad P, Yuvaraj R et al (2023) Blockchain-based conditional privacy-preserving authentication scheme in VANETs. Multimed Tools Appl 82:24155–24179. https://doi.org/10.1007/s11042-022-14288-8

    Article  Google Scholar 

  3. Nicholls J, Kuppa A, Le-Khac NA (2023) SoK: The next phase of identifying illicit activity in Bitcoin. In: Proc IEEE Int Conf Blockchain Cryptocurrency (ICBC), pp 1–10. https://doi.org/10.1109/ICBC56567.2023.10174963

  4. Nakamoto S (2008) Bitcoin: a peer-to-peer electronic cash system. Available at SSRN 3440802. Accessed 11 Sept 2023

  5. Bohme R, Christin N, Edelman B, Moore T (2015) Bitcoin: Economics, technology, and governance. J Economic Perspect 29(2):213–238. https://doi.org/10.1257/jep.29.2.213

    Article  Google Scholar 

  6. Rahouti M, Xiong K, Ghani N (2018) Bitcoin concepts, threats, and machine-learning security solutions. IEEE Access 6:67189–67205. https://doi.org/10.1109/ACCESS.2018.2874539

    Article  Google Scholar 

  7. Panda SK, Sathya AR, Das S (2023) Bitcoin: beginning of the Cryptocurrency era. In: Panda SK, Mishra V, Dash SP, Pani AK (eds) Recent advances in Blockchain Technology. Intelligent systems Reference Library, vol 237. Springer, Cham. https://doi.org/10.1007/978-3-031-22835-3_2

    Chapter  Google Scholar 

  8. Christin N (2013) Traveling the Silk Road: A measurement analysis of a large anonymous online marketplace. In: Proceedings of the 22nd International Conference on World Wide Web, pp 213–224. https://doi.org/10.1145/2488388.2488408

  9. Hout MCV, Bingham T (2013) Silk Road’, the virtual drug marketplace: a single case study of user experiences. Int J Drug Policy 24(5):385–391. https://doi.org/10.1016/j.drugpo.2013.01.005

    Article  Google Scholar 

  10. Martin J (2014) Lost on the Silk Road: online drug distribution and the ‘cryptomarket.’ Criminol Criminal Justice 14(3):351–367. https://doi.org/10.1177/1748895813505234

    Article  Google Scholar 

  11. Karlstrøm H (2014) Do libertarians dream of electric coins? The material embeddedness of Bitcoin. Distinktion: Scandinavian J Social Theory 15(1):23–36. https://doi.org/10.1080/1600910X.2013.870083

    Article  Google Scholar 

  12. Nouman M, Qasim U, Nasir H, Almasoud A, Imran M, Javaid N (2023) Malicious Node Detection Using Machine Learning and Distributed Data Storage Using Blockchain in WSNs. IEEE Access 11:6106–6121. https://doi.org/10.1109/ACCESS.2023.3236983

    Article  Google Scholar 

  13. Meiklejohn S, Pomarole M, Jordan G, Levchenko K, McCoy D, Voelker GM, Savage S (2016) A fistful of bitcoins: characterizing payments among men with no names. Commun ACM 59(4):86–93. https://doi.org/10.1145/2896384

    Article  Google Scholar 

  14. Chaurasia BK, Verma S (2010) Maximising Anonymity of a Vehicle. In: International Journal of Autonomous and Adaptive Communications Systems (IJAACS), Special Issue on: Security, Trust, and Privacy in DTN and Vehicular Communications, Inderscience 3(2):198–216. https://doi.org/10.1504/IJAACS.2010.031091https://doi.org/10.1080/07421222.2016.1205918

  15. Samtani S, Chinn R, Chen H, Nunamaker JF Jr (2017) Exploring emerging hacker assets and key hackers for proactive cyber threat intelligence. J Manage Inform Syst 34(4):1023–1053. https://doi.org/10.1080/07421222.2017.1394049

    Article  Google Scholar 

  16. Andola N, Yadav VK, Venkatesan S, Verma S (2021) Anonymity on blockchain based e-cash protocols—A survey. Comput Sci Rev 40:100394–100411. https://doi.org/10.1016/j.cosrev.2021.100394

    Article  MathSciNet  Google Scholar 

  17. Andola N, Raghav, Yadav VK et al (2021) SpyChain: a Lightweight Blockchain for Authentication and Anonymous authorization in IoD. Wirel Pers Commun 119:343–362. https://doi.org/10.1007/s11277-021-08214-8

    Article  Google Scholar 

  18. Beck R (2018) Beyond bitcoin: The rise of blockchain world. Computer 51(2):54–58

    Article  Google Scholar 

  19. Abbasi A, Zahedi FM, Zeng D, Chen Y, Chen H, Nunamaker JF Jr (2015) Enhancing predictive analytics for anti-phishing by exploiting website genre information. J Manage Inform Syst 31(4):109–157. https://doi.org/10.1080/07421222.2014.1001260

    Article  Google Scholar 

  20. Benjamin V, Zhang B, Nunamaker JF Jr, Chen H (2016) Examining hacker participation length in cybercriminal internet-relay-chat communities. J Manage Inform Syst 33(2):482–510

    Article  Google Scholar 

  21. Abbasi A, Hsinchun C (2005) Applying authorship analysis to extremist-group web forum messages. IEEE Intell Syst 20(5):67–75. https://doi.org/10.1109/mis.2005.81

    Article  Google Scholar 

  22. Beck R, Czepluch JS, Lollike N, Malone S (2016) Blockchain–the gateway to trust-free cryptographic transactions. In: Twenty-Fourth European Conference on Information Systems (ECIS), pp 1–14

  23. Koshy P, Koshy D, McDaniel P (2014) An analysis of anonymity in bitcoin using p2p network traffic. In: Christin N, Safavi-Naini R (eds) Financial Cryptography and Data Security. FC 2014. Lecture Notes in Computer Science() 8437:469–485. https://doi.org/10.1007/978-3-662-45472-5_30

  24. Androulaki E, Karame GO, Roeschlin M, Scherer T, Capkun S (2013) Evaluating user privacy in bitcoin In: Sadeghi AR (eds) Financial Cryptography and Data Security 7859: 34–51. https://doi.org/10.1007/978-3-642-39884-1_4

  25. Bonneau J, Narayanan A, Miller A, Clark J, Kroll JA, Felten EW (2014) Mixcoin: Anonymity for bitcoin with accountable mixes. In: Christin, N., Safavi-Naini, R. (eds) Financial Cryptography and Data Security. FC 2014. Lecture Notes in Computer Science 8437: 486–504. https://doi.org/10.1007/978-3-662-45472-5_31

  26. Misra G, Hazela B, Chaurasia BK (2013) Zero knowledge based authentication for internet of medical things. In: 14th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp 1–6

  27. Meiklejohn S, Orlandi C (2015) Privacy-enhancing overlays in bitcoin. In: International Conference on Financial Cryptography and Data Security, pp 127–141.https://doi.org/10.1007/978-3-662-48051-9_10

  28. Harlev MA, Sun Yin H, Langenheldt KC, Mukkamala R, Vatrapu R (2018) Breaking bad: De-anonymising entity types on the bitcoin blockchain using supervised machine learning. In: Proceedings of the 51st Hawaii International Conference on System Sciences, pp 3497–3506

  29. Zola F, Eguimendia M, Bruse JL, Urrutia RO (2019) Cascading machine learning to attack bitcoin anonymity. In: IEEE International Conference on Blockchain (Blockchain), pp 10–17. https://doi.org/10.1109/Blockchain.2019.00011

  30. Yin HHS, Langenheldt K, Harlev M, Mukkamala RR, Vatrapu R (2019) Regulating cryptocurrencies: a supervised machine Learning Approach to de-anonymizing the Bitcoin Blockchain. J Manage Inform Syst 36(1):37–73. https://doi.org/10.1080/07421222.2018.1550550

    Article  Google Scholar 

  31. Lin YJ, Wu PW, Hsu CH, Tu IP, Liao SW (2019) An evaluation of bitcoin address classification based on transaction history summarization. In: 2019 IEEE International Conference on Blockchain and Cryptocurrency (ICBC), pp 302–310. https://doi.org/10.1109/BLOC.2019.8751410

  32. Lee C, Maharjan S, Ko K, Hong JWK (2020) Toward detecting illegal transactions on Bitcoin using machine-learning methods. In: Zheng Z, Dai HN, Tang M, Chen X (eds) Blockchain and Trustworthy systems. BlockSys 2019. Communications in Computer and Information Science, vol 1156. Springer, Singapore.https://doi.org/10.1007/978-981-15-2777-7_42

    Chapter  Google Scholar 

  33. Li Y, Cai Y, Tian H, Xue G, Zheng Z (2020) Identifying Illicit addresses in Bitcoin Network. In: Zheng Z, Dai HN, Fu X, Chen B (eds) Blockchain and Trustworthy systems. BlockSys 2020, vol 1267. Springer, Singapore. https://doi.org/10.1007/978-981-15-9213-3_8

    Chapter  Google Scholar 

  34. Liu T et al (2020) A new Bitcoin address Association Method using a two-level learner model. In: Wen S, Zomaya A, Yang LT et al (eds) Algorithms and architectures for parallel Processing. ICA3PP 2019, vol 11945. Springer, Cham. https://doi.org/10.1007/978-3-030-38961-1_31

    Chapter  Google Scholar 

  35. Farrugia S, Ellul J, Azzopardi G (2020) Detection of illicit accounts over the Ethereum blockchain. Expert Syst Appl 150:113318. https://doi.org/10.1016/j.eswa.2020.113318

    Article  Google Scholar 

  36. Michalski R, Dziubałtowska D, Macek P (2020) Revealing the character of nodes in a blockchain with supervised learning. IEEE Access 8:109639–109647. https://doi.org/10.1109/ACCESS.2020.3001676

    Article  Google Scholar 

  37. Poursafaei F, Hamad GB, Zilic Z (2020) Detecting malicious Ethereum entities via application of machine learning classification. In: 2nd IEEE Conference on Blockchain Research & Applications for Innovative Networks and Services (BRAINS), pp 120–127

  38. Kang C, Lee C, Ko K, Woo J, Hong JWK (2020) De-anonymization of the Bitcoin Network using address clustering. In: Zheng Z, Dai HN, Fu X, Chen B (eds) Blockchain and Trustworthy systems. BlockSys 2020, vol 1267. Springer, Singapore. https://doi.org/10.1007/978-981-15-9213-3_38

    Chapter  Google Scholar 

  39. Ibrahim RF, Elian AM, Ababneh M (2021) Illicit account detection in the ethereum blockchain using machine learning. In: 2021 InternationaL Conference on Information Technology (ICIT), pp 488–493. https://doi.org/10.1109/ICIT52682.2021.9491653

  40. Elbaghdadi A, Mezroui S, El Oualkadi A (2021) K-Nearest Neighbors Algorithm (KNN): An approach to detect illicit transaction in the bitcoin network. In: Azevedo A, Santos M (eds) Integration Challenges for Analytics, Business Intelligence, and Data Mining, (pp 161–178). IGI Global. https://doi.org/10.4018/978-1-7998-5781-5.ch008

  41. Nerurkar P, Bhirud S, Patel D, Ludinard R, Busnel Y, Kumari S (2021) Supervised learning model for identifying illegal activities in Bitcoin. Appl Intell 51:3824–3843. https://doi.org/10.1007/s10489-020-02048-w

    Article  Google Scholar 

  42. Jatoth C, Jain R, Fiore U, Chatharasupalli S (2022) Improved classification of Blockchain transactions using feature Engineering and Ensemble Learning. Future Internet 14(1):16. https://doi.org/10.3390/fi14010016

    Article  Google Scholar 

  43. Nerurkar P (2023) Illegal activity detection on bitcoin transaction using deep learning. Soft Comput 27:5503–5520. https://doi.org/10.1007/s00500-022-07779-1

    Article  Google Scholar 

  44. De Juan Fidalgo P, Cámara C, Peris-Lopez P (2023) Generation and Classification of Illicit Bitcoin Transactions. In: Bravo J, Ochoa S, Favela J (eds) Proceedings of the International Conference on Ubiquitous Computing & Ambient Intelligence (UCAmI 2022). UCAmI 2022. Lecture Notes in Networks and Systems, vol 594. Springer, Cham. https://doi.org/10.1007/978-3-031-21333-5_108

  45. Sharma AK, Peelam MS, Chaurasia BK, Chamola V (2023) QIoTChain: Quantum IoT-blockchain fusion for advanced data protection in Industry 4.0. IET Blockchain published by John Wiley & Sons Ltd, pp 1–11.https://doi.org/10.1049/blc2.12059

  46. Al-Hashedi KG et al (2023) A supervised model to detect suspicious activities in the bitcoin network. In: Al-Sharafi MA, Al-Emran M, Al-Kabi MN, Shaalan K (eds) Proceedings of the 2nd International Conference on Emerging Technologies and Intelligent Systems. ICETIS 2022. Lecture Notes in Networks and Systems, vol 584. Springer, Cham. https://doi.org/10.1007/978-3-031-25274-7_53

  47. Umer Q, Li JW, Ashraf MR, Bashir RN, Ghous H (2023) Ensemble deep learning based prediction of fraudulent Cryptocurrency transactions. IEEE Access. https://doi.org/10.1109/ACCESS.2023.3310576

    Article  Google Scholar 

  48. Khalilov MCK, Levi A (2018) A survey on anonymity and privacy in bitcoin-like digital cash systems. IEEE Commun Surv Tutorials 20(3):2543–2585. https://doi.org/10.1109/COMST.2018.2818623

    Article  Google Scholar 

  49. Reiter MK, Rubin AD (1998) Crowds: anonymity for Web transactions. ACM Transactions on Information and System Security (TISSEC) 1(1):66–92. https://doi.org/10.1145/290163.290168

    Article  Google Scholar 

  50. Chaurasia BK, Verma S, Tomar GS (2013) Intersection attack on anonymity in VANET. In: Gavrilova ML, Tan CJK (eds) Transactions on Computational Science XVII, Springer-Verlag Berlin Heidelberg 7420:133–149. https://doi.org/10.1007/978-3-642-35840-1_7

  51. Wu X, Bertino E (2007) An analysis study on Zone-based Anonymous Communication in Mobile Ad Hoc Networks. IEEE Trans Dependable Secure Comput 4(4):252–264. https://doi.org/10.1109/TDSC.2007.70213

    Article  Google Scholar 

  52. Froomkin AM (1995) Anonymity and its enmities. Journal of Online Law, Online available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2715621. Accessed 15 May 2023

  53. Froomkin AM (1999) Legal issues in anonymity and pseudonymity. Inform Soc 15(2):113–127. https://doi.org/10.1080/019722499128574

    Article  Google Scholar 

  54. Cui J, Huang C, Meng H, Wei R (2023) Tor network anonymity evaluation based on node anonymity. Cybersecurity 6(55):1–16. https://doi.org/10.1186/s42400-023-00191-8

    Article  Google Scholar 

  55. Zhang W, Lu T, Du Z (2021) TNRAS: Tor nodes reliability analysis scheme. In: The 11th International Conference on Communication and Network Security, pp 21–26

  56. Schnitzler T, Pöpper C, Dürmuth M, Kohls K (2021) We built this circuit: Exploring threat vectors in circuit establishment in Tor. In: 2021 IEEE European Symposium on Security and Privacy (EuroS&P), pp 319–336

  57. Mienye ID, Sun Y (2022) A survey of ensemble learning: concepts, algorithms, applications, and prospects. IEEE Access 10:99129–99149. https://doi.org/10.1109/ACCESS.2022.3207287

    Article  Google Scholar 

  58. Chaurasia BK, Raj H, Rathour SS, Singh PB (2023) Transfer learning driven ensemble model for detection of diabetic retinopathy disease. In Medical, Biological Engineering and Computing, Springer 61:2033–2049. https://doi.org/10.1007/s11517-023-02863-6

    Article  Google Scholar 

  59. Zhou ZH (2012) Ensemble methods: foundations and algorithms, 1st edn. Chapman and Hall/CRC. https://doi.org/10.1201/b12207

  60. Freund Y, Schapire RE (1996) Experiments with a new boosting algorithm. In: ICML 96:148–156. Online Available at: https://cseweb.ucsd.edu/~yfreund/papers/boostingexperiments.pdf. Accessed 17 Sept 2023

  61. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J (2011) Scikit-learn: Machine learning in Python. In: The Journal of machine Learning research 12:2825–2830. Online Available at: https://jmlr.org/papers/volume12/pedregosa11a/pedregosa11a.pdf. Accessed 17 Sept 2023

  62. Merkle RC (2019) Protocols for public key cryptosystems. IEEE Symposium on Security and Privacy, pp 122–134. https://doi.org/10.1109/SP.1980.10006

  63. Andrychowicz M, Dziembowski S, Malinowski D, Mazurek Ł (2015) On the Malleability of Bitcoin Transactions. In: Brenner M, Christin N, Johnson B, Rohloff K (eds) Financial Cryptography and Data Security. Lecture Notes in Computer Science 8976: 1–18. https://doi.org/10.1007/978-3-662-48051-9_1

  64. Blockchair Database, Online available at: https://gz.blockchair.com/bitcoin/transactions. Accessed 29 Mar 2023

  65. WalletExplorer, Online available at: https://www.walletexplorer.com/. Accessed 30 Mar 2023

  66. Beautiful Soup, Online available at: https://www.browserstack.com/guide/web-scraping-using-beautiful-soup. Accessed 30 Mar 2023

  67. LabelEncoder, Online available at: https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html. Accessed 23 Apr 2023

  68. Singh D, Singh B (2020) Investigating the impact of data normalization on classification performance. Appl Soft Comput 97:105524

    Article  Google Scholar 

  69. García S, Luengo J, Herrera F (2015) Data preprocessing in data mining, vol 72. Springer International Publishing, Cham, Switzerland, pp 59–139

    Google Scholar 

  70. Han J, Pei, J, Tong, H (2012) Data mining: concepts and techniques. Morgan Kaufmann. https://doi.org/10.1016/C2009-0-61819-5

  71. Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 1(16):321–357. https://doi.org/10.1613/jair.953

    Article  Google Scholar 

  72. Saxena R, Arora D, Nagar V (2023) Classifying blockchain cybercriminal transactions using hyperparameter tuned supervised machine learning models. Int J Comput Sci Eng 26(6):615–626. https://doi.org/10.1504/IJCSE.2022.10056854

    Article  Google Scholar 

  73. Batista GE, Prati RC, Monard MC (2004) A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD Explorations Newsl 6(1):20–29. https://doi.org/10.1145/1007730.1007735

    Article  Google Scholar 

  74. SMOTE Module, Online available at: https://imbalanced-learn.org/stable/references/generated/imblearn.over_sampling.SMOTE.html. Accessed on 23/04/2023

  75. RandomOverSampler, Online available at: https://imbalanced-learn.org/stable/references/generated/imblearn.over_sampling.RandomOverSampler.html. Accessed 23/04/2023

  76. RandomUnderSampler, Online available at https://imbalanced-learn.org/stable/references/generated/imblearn.under_sampling.RandomUnderSampler.html. Accessed 23/04/2023

  77. Schratz P, Muenchow J, Iturritxa E, Richter J, Brenning A (2019) Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data. Ecol Model 406:109–120. https://doi.org/10.1016/j.ecolmodel.2019.06.002

    Article  Google Scholar 

  78. Probst P, Wright MN, Boulesteix AL (2019) Hyperparameters and tuning strategies for random forest. Wiley Interdisciplinary Reviews: Data Min Knowl Discovery 9(3):1–15. https://doi.org/10.1002/widm.1301

    Article  Google Scholar 

  79. Zhang L, Zhan C (2017) Machine learning in rock facies classification: an application of XGBoost. In: International Geophysical Conference on Society of Exploration Geophysicists and Chinese Petroleum Society, pp 1371–1374. https://doi.org/10.1190/IGC2017-351

  80. Bajpai S, Sharma K, Chaurasia BK (2023) Intrusion detection Framework in IoT Networks. Springer Nature Computer Science Journal. Special Issue Mach Learn Smart Syst 4(350):1–17. https://doi.org/10.1007/s42979-023-01770-9

    Article  Google Scholar 

  81. Liashchynskyi P, Liashchynskyi P (2019) Grid search, random search, genetic algorithm: a big comparison for NAS. Online available at https://arxiv.org/pdf/1912.06059.pdf. Accessed 12/02/ 2023 

  82. Putatunda S, Rama K (2018) A comparative analysis of hyperopt as against other approaches for hyper-parameter optimization of XGBoost. In: Proceedings of the 2018 International Conference on Signal Processing and Machine Learning, pp 6–10. https://doi.org/10.1145/3297067.3297080

  83. RandomizedSearchCV Online available at: https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.RandomizedSearchCV.html. Accessed 23/04/2023

  84. Syarif I, Prugel-Bennett A, Wills G (2016) SVM parameter optimization using grid search and genetic algorithm to improve classification performance. (TELKOMNIKA) Telecommun Comput Electronics Control 14(4): 1502–1509. https://doi.org/10.12928/TELKOMNIKA.v14i4.3956

  85. Ataei M, Osanloo M (2004) Using a combination of genetic algorithm and the Grid Search Method to Determine Optimum Cutoff grades of multiple metal deposits. Int J Surf Min Reclam Environ 18(1):60–78. https://doi.org/10.1076/ijsm.18.1.60.23543

    Article  Google Scholar 

  86. Xiao T, Ren D, Lei S, Zhang J, Liu X (2014) Based on grid-search and PSO parameter optimization for Support Vector Machine. In: Proceeding of the 11th World Congress on Intelligent Control and Automation, pp 1529–1533. https://doi.org/10.1109/WCICA.2014.7052946

  87. GridSearchCV Online available at https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html. Accessed 23/04/2023

  88. Aversana PD (2019) Comparison of different machine learning algorithms for lithofacies classification from well logs. Bollettino Di Geofis Teorica Ed Appl 60(1):69–80. https://doi.org/10.4430/bgta0256

    Article  Google Scholar 

Download references

Funding

The authors have not received funding from any of the sources.

Author information

Authors and Affiliations

  1. Amity University Uttar Pradesh, Lucknow, India

    Rohit Saxena & Deepak Arora

  2. Pranveer Singh Institute of Technology, Kanpur, UP, India

    Rohit Saxena, Vishal Nagar & Brijesh Kumar Chaurasia

Authors
  1. Rohit Saxena
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Deepak Arora
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vishal Nagar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Brijesh Kumar Chaurasia
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The idea and problem formulation along with proposed solution, result analysis, and by corresponding author & supervisor, and verified by all other authors.

Corresponding author

Correspondence to Brijesh Kumar Chaurasia.

Ethics declarations

Conflict of interest

The work is not submitted in any other journal. There is no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Saxena, R., Arora, D., Nagar, V. et al. Blockchain transaction deanonymization using ensemble learning. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-19233-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11042-024-19233-5

Keywords

Search

Navigation