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Comprehensive framework for implementing blockchain-enabled federated learning and full homomorphic encryption for chatbot security system

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Abstract

Chatbot is an artificial intelligence application that can provide a conversational environment between humans and machines. Most organizations and industries are willing to lay out their services through chatbots because they can provide 24/7 customer support. Meanwhile, it raises security and privacy challenges like access control, data leakage during transmission, SQL injection attacks, and language model attacks, which make the users concerned about their data, performance, and accuracy. Therefore, this research paper proposes a comprehensive framework integrating blockchain, federated learning, and a fully homomorphic encryption algorithm with face recognition to solve the above-mentioned chatbot’s challenges. The experimental result shows that a distributed system improves chatbot accuracy (90%) and that more transactions in less time with more clients do not affect the performance. In contrast, more iterations and clients will decrease the accuracy, performance, and transactions in a centralized system. In addition, fully homomorphic encryption improves and speeds up the data encryption process. It encrypted more data (1792 MB) in a small amount of 1240 times per second, and conversations and transactions can be transferred via a secure network to ensure the confidentiality, integrity, and authenticity of users’ data. The implementation of such a comprehensive framework in real-life situations can improve chatbot security when it actively works as a customer agent in an organization.

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All data sets analysed during this study to support the results of the article are publicly available.

References

  1. Asbjorn Folstad, Cecilie Bertiussen Nordheim, & Cato Alexander Bjorkli, What makes users trust a chatbot for customer services? an exploratory interview study, in The proceeding fof the Fifth International conference on Internet Science, Oslo, Norway (2018)

  2. Lorenz Cuno Klopfenstein, Saverio Delpriori, Silvia Malatini, Aessandro Bogliolo, The Rise of Bots: A Survey of Conversational Interfaces, patterns, and Paradiams, in DIS17: Proceedings of the 2017 Conference on Designing Interactiv System (2017)

  3. Gentsch, P.: Conversational AI: How (Chat)Bots Will Reshape the Digital Experience. In: AI in Marketing Sales and Services: How Marketers without a Data Science Degree can user AI, Big Data and Bots, Frankfurt, pp. 81–95. SpringerLink, Germany (2019)

    Chapter  Google Scholar 

  4. Daniel Adiwardana, Minh-Thang Luong, David R. So, et al, Towards a human-like open-domain chatbot, arXiv:2001.09977v3 [cs.CL] 27 Feb 2020 (2020)

  5. Okuda, T., Shoda, S.: AI-Based chatbot service for financial industry. FUJITSU Scientific and Technical Journal 54(2), 4–8 (2018)

    Google Scholar 

  6. Milind H Shah, mahesh Panchal, Theoretical evaluation of securing modules for educational chatbot, in Proceedings of the Sixth International Conference on Intelligent Computing and Control System (ICICCS) (2022)

  7. Kuhail, M.A., Alturki, N., Alramlawi, S., Alhejori, K.: Interacting with educational chatbots: a systematic review. Educ. Inf. Technol. 28, 973–1018 (2023)

    Article  Google Scholar 

  8. Asim Mohammed Eltahir, hussam Abdullah, Jan Platos, and Vaclav Snasel, Review of chatbot security systems, in 2022 26th International Conference on Circuits, Systems, Communications and Computers (CSCC) (2022)

  9. Rahime Belen Saglam, Jason R.C., Nurse, and Duncan Hodges Privacy Concerns in Chatbot Interactions: When to Trust and When to Worry, in 23rd HCI International Conference, HCII 2021, Swithzerland (2021)

  10. Nazar Waheed, Muhammad Ikram, Saad Sajid Hashmi, et al, An empirical assessment of security and privacy risks of web-based chatbots, WISE international Journal, pp. 1–25 (2022)

  11. Erik Derner, and Kristina Batistic, Beyond the safguards: exploring the security risks of chatGPT, arXiv:2305.08005v1 (2023)

  12. Hoy, M.B.: Alexa, siri, cartana, and more: an introduction to voice assistants. Med. Ref. Serv. Q. 37(1), 81–88 (2018)

    Article  Google Scholar 

  13. Fabio Clarizia, Francesco Colace, Marco Lombardi, et al, Chatbot: An education support system for students, in International Symposium on Cyberspace Safety and Security (2018)

  14. Okonkwo, C.W., Ade-Ibijola, A.: Chatbots applications in education: a systematic review. Comptuers and Education: Artificial Intelligence 2, 100033 (2021)

    Google Scholar 

  15. Huang, X.: Chatbot: design, architecture, and applications. Univesity of Pennsylvania, School of Engineering and Applied Science, Pennsylvania (2021)

    Google Scholar 

  16. Dasagrandhi, Understanding The conversational chatbot architecture, V-Soft consulting, 2550 Eastpoint Pkwy Suite 300 Luisville, Ky 40223 (2020)

  17. Haristiani, Nuria, Artificial Intelligence (AI) Chatbots as Language Learning Medium: An inquiry, in International Conference on Education, Science and Technology 2019, Gothenburg, Sweden (2019)

  18. Jing Xu, Da Ju, Margaret Li, et al, Bot-Adversarial Dialogue for Safe Conversational Agents, in Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, New York. (2021)

  19. Domna Bilika, Nikoletta Michopoulou, et al, “Hello me, meet the real me: audio deepfake attacks on voice assistants, arXiv:2302.10328v1 [cs.CR] (2023)

  20. Chung, H., Lorga, M., Voas, J., Lee, S.: Alexa, can i trust you? IEEE Xplore 9, 100–105 (2017)

    Google Scholar 

  21. Krishna Gondaliya, Sergey Butakov, and Pavol Zavarsky, SLA as Mechanism to Manage Risks Related to Chatbot Services, in 2020 IEEE 6th Intl Conference on Big Data Security on Cloud (BigDataSecurity) (2020)

  22. Cheng Qian, Haode Qi, Gengyu Wang, et al, Distinguish Sens from Nonsens:Out-of-Scope Detection for Virtual Assistants, arXiv:2301.06544v1 [cs.CL] 16 Jan 2023 (2023)

  23. Zheng, Y., Feng, XiaoYi, Xia, Z., et al.: Why adversarial reprogramming works, when it fails, and how to tell the difference. Inf. Sci. 632, 130–143 (2023)

    Article  Google Scholar 

  24. Stefano Bistarelli, Francesco Santini, and Carlo Taticchi (2020) A Chatbot Extended with Argumentation, in 5th workshop on Advances In Argumentation In Artificial Intelligence, Milano, Italy

  25. Kadayan, N.S., Dave, A., Vinit, K.: Chatbot using deep learning. J. Emerg. Technol. Innov. Res. 6(2), 137–141 (2019)

    Google Scholar 

  26. Wube, H.E., Esubalew, S.Z., et al.: Text-based chatbot in financial sector: a systematic literature review. Data Science in Finance and Economics 2(3), 209–236 (2022)

    Article  Google Scholar 

  27. Nivila, A., Sujitha, S., Prithika, N., Gnana Prakash, V.: Design of chatbots using deep learning. International Research Journal of Engineering and Technology (IRJET) 9(20), 1105–1110 (2022)

    Google Scholar 

  28. Kidwai, B., Nadesh, R.K.: Design and development of diagnostic chatbot for supporting primary health care system. Procedia Computer Science 167, 75–84 (2020)

    Article  Google Scholar 

  29. Aishwarya Surani, and Sanchari Das, Understanding privacy and security postures of healthcare chatbots, Association for Computing Machinery (ACM) (2022)

  30. Hamza Harkous, Kassem Fawaz, Kang G. Shin, Karl Aberer, PriBots: conversational privacy with chatbots, in Workshop on the Future of Privacy Indicators, at the Twelfth Symposium on Usable Privacy and Security (SOUPS), Denver, Colorado (2016)

  31. Md. Saiful Islam Bhuiyan, Abdur Razzak, Md Sadek Ferdous, et al, BONIK: A Blockchain Empowered Chatbot for Financial Transactions, in Conference on Trust, Security and Privacy in Computing and Communications (2020)

  32. Martin Hasal, Jana Nowakova, et al, Chatbots: Security, Privacy, Data protection, and Social aspects, Wiley, pp. 1–13 (2021)

  33. Lee, M., Frank, L., IJsseelsteijin, W.: Brokerbot: a cryptocurrency chatbot in the social-technical gap of trust. Computer Supported Cooperative Work (CSCW) 30, 79–117 (2021)

    Article  Google Scholar 

  34. Cai, S., Han, D., Li, D., Zheng, Z., Crespi, N.: An reinforcement learning-based speach censorship chatbot system. J. Supercomput. 78, 8751–8773 (2022)

    Article  Google Scholar 

  35. Cordero, J., Barba-Guaman, L., Guaman, F.: Use of chatbots for customer serivces in MSMEs. Applied Computing and Informatics (2022). https://doi.org/10.1108/ACI-06-2022-0148

    Article  Google Scholar 

  36. Zumstein, D., Hundertmark, S.: Chatbots-and interactive technology for personalized communication, transactions and services. IADIS International Journal on WWW/Internet 15(1), 96–109 (2018)

    Google Scholar 

  37. Jide Edu, Cliona Mulligan, and Fabio Pierazzi, Exploring the security and privacy risk of chatbots in messaging services, in Proceeding of the 22nd ACM Internet Measurement Conference (IMC’22), France (2022)

  38. Sen-Tarng Lai, Fang-Yie Leu, and Jeng-Wei Lin, A banking chatbot security control procedure for protecting user data security and privacy, in 13th International Conference on Broadband and Wireless Computing Communication and Applications (BWCCA-2018), Switzerland (2019)

  39. de Cosmo, L.M., Piper, L., de Vittorio, A.: The role of attitude toward chatbots and privacy concern on the relationship between attitude toward mobile advertising and behavioral intent to use chatbots. Ital. J. Mark. 2021, 83–102 (2021)

    Article  Google Scholar 

  40. Josip Bozic, & Franz Wotawa, Security testing for chatbots, in In IFIP interanation Conference on Testing Software and System (ICTSS), Switzerland (2018)

  41. Jide Edu, Cliona Mulligan, Fabio Pierazzi, et al, Exploring the Security and Privacy Risks of Chatbots in Messaging Services, in In Proceedings of the 22nd ACM Internet Measurement Conference (IMC ‘22), New York, USA (2022)

  42. Koien, M.A., Geir, M.: Cyber security and the internet of things: vulnerabilities, threats, intruders and attacks. Journal of Cyber Security 4, 65–88 (2015)

    Google Scholar 

  43. Kar, R., Haldar, R.: Applying chatbots to the internet of things: opportunities and architectureal elements. International Journal of Advanced Comptuer Science and Application (IJACSA) (2018). https://doi.org/10.14569/IJACSA.2016.071119

    Article  Google Scholar 

  44. Mallik, A., Ahsan, A., et al.: Man-in-the-middle attack: understanding in simple words. International Jouranl of Data and Network Science 3, 77–92 (2019)

    Article  Google Scholar 

  45. Laura Feinstein, Dan Schnackenberg, et al, Statistical approaches to ddos attack detections and response, in Proceedings of the DARPA Information Survivability Conference and Exposition (2013)

  46. Yameen Ajani, Krish Mangalorkar, Yohann Nadar, et al, Homomorphic encryption for Secure Conversation with AI bots over cloud to prevent Social Engineering attacks, International Journal of Engineering Research and Applications, pp. 21–27 (2021)

  47. Pathrabe, Trupti V. Survey on security issues of growing technology: big data, in National Conference on Latest Trends in Networking and Cyber Security (2017)

  48. Jung, S.: Semantic vector learning for natural language understanding. Science Direct 56, 130–145 (2019)

    Google Scholar 

  49. Eric Wallace, Shi Feng, et al, Universal adversarial triggers for attacking and analyzing NLP, in Proceedings of the 2019 Conferene on Empirical Methods in natural Language Processing, Hong Kong, China (2019)

  50. Tian, J., Wang, B., Guo, R., et al.: Advarsarial attacks and defenses for deep-learning-based unmanned aerial vehicles. IEEE Internet Things J. 9(22), 22399–22409 (2022)

    Article  Google Scholar 

  51. Tiam, J., Wang, B., Wang, Z., et al.: Joint adversarial example and false data injections attacks for state estimation in power systems. IEEE Transactions on Cybernetics 52(12), 13699–13713 (2022)

    Article  Google Scholar 

  52. Yang, J., Chen, Y.-L., Por, L.Y., Chin Soon, Ku.: A systematic literature review of information security in chatbots. Appl. Sci. 13, 2–18 (2023)

    Google Scholar 

  53. Michael Lahzi Gaid, Mohamed Waleed Fakhr, and Gamal Ibrahim Selim, Secure Translation Using Fully Homomorphic Encryption and Sequence-to-Sequence Neural Network, in 28th International Conference on Computer Theory and Application (ICCTA), Alexandria: Egypt (2018)

  54. Jalali, N.A., Chen, H.: Federated learning security and privacy-preserving algorithm and experimental research under internet of things critical infrastracture. Tsinghua Science and Technology 29(2), 400–414 (2023)

    Article  Google Scholar 

  55. Acar, A., Aksu, H., Uluagac, A.S.: A survey on homomorphic encryption scheme: theory and implementation. ACM Comput. Surv. 51(4), 79 (2018)

    Google Scholar 

  56. Romanov, D.: Secure multi-party computation for supply chain collaboration. University of Technology, Delft (2021)

    Google Scholar 

  57. Jalali, N.A., Chen, H.: Security issues and solutions in federated learning under IoT critical infrastructure. Wireless Personal Communication 129, 475–500 (2022)

    Article  Google Scholar 

  58. Sebastian, Glorin (2022) Privacy and data protection in ChatGPT and other AI Chatbots: strategies for securing user information, Georgia Institiute of Technology

  59. Jalali, N.A., Chen, H.: Security issues and solutions in federated learning under IoT critical infrastructure. Wirel. Pers. Commun. 129, 475–500 (2023)

    Article  Google Scholar 

  60. Kairouz, P., Mcmahan, H.B., Avent, B., et al.: Advanced and open problems in federated learning. Found. Trends Mach. Learn. 14(1–2), 1–210 (2021)

    Article  Google Scholar 

  61. Renhao, Lu., Zhang, W., Li, Q., et al.: Adaptive asynchronous federated learning. Futur. Gener. Comput. Syst. 152, 193–206 (2023)

    Google Scholar 

  62. Zibin Zheng, Shaoan Xie, Hongning Dai, et al, An overview of blockchain technology: architecture. Consensus, and Future Trends, in 2017 IEEE 6th International Congress on Big Data (2017)

  63. Lukman Adewale Ajao, Simon Tooswem Apeh, Blockchain integration with machine learning for securing fog computing vulnerability in smart city sustainability, in 1st International Conference on Advanced Innovations in Smart Cities ( ICAISC), Jeddah: Saudi Arabia (2023)

  64. Khalid, U., Asim, M., Baker, T., et al.: A decentralized lightweight blockchain-based authentication mechanism for IoT system. Liverpool John Moores University (Cluster Computing) 23, 2067–2087 (2020)

    Google Scholar 

  65. Mohan, V.S., Sankaran, S., Nanda, P., Achuthan, K.: Enabling secure lightweight mobile Narrowband Internet of Things (NB-IoT) applications using blockchain. Journal of Network and Computer Application 219, 103723 (2023)

    Article  Google Scholar 

  66. Ngabo, D., Wang, D., Iwendi, C., Anajemba, J.H., Ajao, L.A., Biamba, C.: Blockchain-based security mechanism for the medical data at fog computing architecture of internet of things. Electronics 11(17), 2110 (2021)

    Article  Google Scholar 

  67. Guang Chen, Bing Xu, Manli Lu, and Nian-Shing Chen, Exploring Blockchain Technology and Its Potential Applications for Education, Springer Link, vol. 5, no. 1 (2018)

  68. Kshetri, N.: Blockchain’s roles in strngthening cybersecurity and protecting privacy. Elsevier (Telecommunications Policy) 41(10), 1027–1038 (2017)

    Article  Google Scholar 

  69. Lukman Adewale Ajao, Buhari Ugbede Umar, Daniel Oluwaseun Olajide, Sanjay Misra, Blockchain Applications in the Smart Era, in Application of Crypto-Blockchain Technology for Securing Electronic Voting System, A. K. T. Sanjay Misra, Ed., EAI/Springer Innovations in Communication and Computing Springer: Cham (2022)

  70. Winson Ye, & Qun Li, Chatbot security and privacy in the age of personal assistants, in 2020 IEEE/ACM Symoposium on Edge Computing (SEC) (2020)

  71. Voege, P., AbuSulayman, I.I.M., Ouda, A.: Smart chatbot for user authentication. Electronics 11, 4016 (2022)

    Article  Google Scholar 

  72. Aneesa, M.P., Sabina, N., Meera, K.: Face recognition using CNN: a systematic review. Int. J. Eng. Res. Technol. 11(6), 182–185 (2022)

    Google Scholar 

  73. Endeley, R.E.: End-to-End encryption in messaging services and national security-case of whatsapp messanger. J. Inf. Secur. 9, 95–99 (2018)

    Google Scholar 

  74. Gentry, G.: A fully homomorphic encryption scheme. Stanford University, California (2009)

    Google Scholar 

  75. Blatt, M., Gusev, A., et al.: Secure large-scale genome-wide association studies using homomorphic encryption. Proc. Natl. Acad. Sci. U.S.A. 117(21), 11608–11613 (2020)

    Article  Google Scholar 

  76. Bani Salamah, J.N., Salameh, J.B., Altarawneh, M.: Evaluation of cloud computing platform for image processing algorithm. J. Eng. Sci. Technol. 14(4), 2345–2358 (2019)

    Google Scholar 

  77. Ajani, Y., Mangalorkar, K., et al.: Homomorphic encryption technology for securing data in cloud computing: a survey. Int. J. Comput. Appl. 160(6), 1–5 (2017)

    Google Scholar 

  78. Singh, S.K., Yang, L.T., Park, J.H.: FusionFedBlock: fusion of blockchain and federated learning to preserve privacy in industry 5.0. Inforamtion Fusion 90, 233–240 (2023)

    Article  Google Scholar 

  79. Mazzei, D., Baldi, G., Fantoni, G., et al.: A blockchain tokenizer for industrial IoT trustless application. Futur. Gener. Comput. Syst. 105, 432–445 (2020)

    Article  Google Scholar 

  80. Chenhao, Xu., Youyang, Qu., Xiang, Y., Gao, L.: Asynchronous federated learning on heterogeneous devices: a survey. Comput. Sci. Rev. 50, 100595 (2023)

    Article  Google Scholar 

  81. Ferdous, S., Chowdhury, F., Alssafi, M.O.: In search of self-sovereign identity leveraging blockchain technology. IEEE Access 7, 103059–103079 (2019)

    Article  Google Scholar 

  82. Abylay Satybaldy, & Mariusz Nowostawski, Reveiw of Technical for Privacy-Preserving Blockchain Systems, in Proceeding of the 2nd ACM International Symposium on Blockchain and Secure Critical Infrastructure, Taipei, Taiwan (2020)

  83. Bharimalla, P.K., Choudhury, H., et al.: A blokchain and NLP based electroni health record system: Indian subcontinent context. Informatica 45, 605–616 (2021)

    Article  Google Scholar 

  84. Kun Shao, Yu., Zhang, J.Y., Li, X., Liu, H.: The triggers that open the NLP model backdoors are hidden in the advaresial samples. Comptuer & Security 118, 102730 (2022)

    Article  Google Scholar 

  85. Kaur, R., Gabrijelcic, D., Klobucar, T.: Artificial intelligence for cybersecurity: literature review and future research direction. Information Fusion 97, 101804 (2023)

    Article  Google Scholar 

  86. Chaka, C.: Geerative AI Chatbots-ChatGPT versus YouChat versus Chatsonic: use case of selected areas of applied English language studies. International Journal of Learning, Teaching and Eduactioanl Research 22(6), 1–19 (2023)

    Article  Google Scholar 

  87. AI-HawawrehAljuhaniJararweh, M.A.Y.: Chatgpt for cybersecurity: practical applications, challenges and future directions. Clust. Comput. 26, 3421–3436 (2023)

    Article  Google Scholar 

  88. Kocon, J., Cichecki, I., Kaszyca, O., et al.: ChatGPT : jack of all trades, master of none. Information Fusion 99, 101861 (2023)

    Article  Google Scholar 

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Acknowledgements

This work was supported by the National Key Research and Development Program of China (No.2023YFC3303803 and 2023YFC3303800), State Key Laboratory of Public Big Data of Guizhou University (No.PBD2023-24), CCF NSFocus Kunpeng Foundation (No.CCF-NSFocus2023012) , Fundamental Research Funds for the Central Universities (No. FRF-AT-19-009Z and FRF-AT-20-11) from the Ministry of Education of China.

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National Key Research and Development Program of China, 2023YFC3303803

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  1. Department of Computer Science, University of Science and Technology Beijing (USTB), Beijing, 100083, China

    Nasir Ahmad Jalali & Chen Hongsong

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  1. Nasir Ahmad Jalali
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All authors contributed equally to the conceptualization and design of the solution for mentioned challenges. Data collection and analysis performed by Nasir Ahmad Jalali and Professor Chen Hongsong provide supervision as well as reviewed the paper for quality improvement.

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Correspondence to Chen Hongsong.

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Jalali, N.A., Hongsong, C. Comprehensive framework for implementing blockchain-enabled federated learning and full homomorphic encryption for chatbot security system. Cluster Comput (2024). https://doi.org/10.1007/s10586-024-04515-2

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