Research Article
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Protecting Privacy and Ethics in AI-driven Conversational Systems using Laplace Mechanism

Year 2025, Volume: 9 Issue: 4 , 779 - 790 , 08.10.2025

Kavita Arora , Neha Gupta , Kamal Upreti , Rituraj Jain , G V Radhakrishnan

https://doi.org/10.31127/tuje.1690888
https://izlik.org/JA43AU83CX

Abstract

AI-powered analytics and conversational systems designed for data mining and other manipulative tasks raise concerns about who can access data and how to utilize it. This data progression has brought forth significant ethical considerations concerning privacy and ethics. AI-powered conversational agents, such as chatbots and virtual assistants, collect and process vast amounts of personal data to enhance user experience and provide tailored responses. Many industries have been transformed by artificial intelligence (AI)-based analytics systems, and some have unthinkable analytical data in terms of better decision-making, highly personalized customer experiences, and improved operational efficiency. While conversational AI offers convenience for users, this technology is also associated with privacy and data protection threats. This research seeks to understand the ethical issues with AI-driven analytics, focusing on data privacy and ethics in verbal and written interactions. This work will look at the current state and potential threats in depth and demonstrate the implementation of differential privacy using the Laplace mechanism in the query output so that the document has no special meaning and does not distort the released results significantly.

Keywords

Algorithms Analytics Artificial Intelligence Data Privacy

References

  • Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399. https://doi.org/10.1038/s42256-019-0088-2
  • Campolo, A., Sanfilippo, M. R., Whittaker, M., & Crawford, K. (2017). AI Now 2017 Report. In https://ainowinstitute.org/wp-content/uploads/2023/04/AI_Now_2018_Report.pdf. https://www.microsoft.com/en-us/research/uploads/prod/2018/02/AI_Now_2017_Report.pdf
  • Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on Bias and Fairness in Machine Learning. ACM Computing Surveys, 54(6), 1–35. https://doi.org/10.1145/3457607
  • Kumar, G. S., Premalatha, K., Maheshwari, G. U., Kanna, P. R., Vijaya, G., & Nivaashini, M. (2023). Differential privacy scheme using Laplace mechanism and statistical method computation in deep neural network for privacy preservation. Engineering Applications of Artificial Intelligence, 128, 107399. https://doi.org/10.1016/j.engappai.2023.107399
  • Goncalves, M., Hu, Y., Aliagas, I., & Cerdá, L. M. (2024). Neuromarketing algorithms’ consumer privacy and ethical considerations: challenges and opportunities. Cogent Business & Management, 11(1). https://doi.org/10.1080/23311975.2024.2333063
  • Liu, Q., Shakya, R., Khalil, M., & Jovanovic, J. (2025). Advancing privacy in learning analytics using differential privacy. In LAK ’25: Proceedings of the 15th International Learning Analytics and Knowledge Conference, 181–191. https://doi.org/10.1145/3706468.3706493
  • Dignum, V. (2018). Ethics in artificial intelligence: introduction to the special issue. Ethics and Information Technology, 20(1), 1–3. https://doi.org/10.1007/s10676-018-9450-z
  • Chaudhuri, K., Guo, C., Rabbat, M., & Meta AI. (2022). Privacy-Aware compression for federated data analysis. In Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence, PMLR 180:296–306. https://proceedings.mlr.press/v180/chaudhuri22a/chaudhuri22a.pdf
  • Stahl, B. C. (2021). Ethical issues of AI. In SpringerBriefs in research and innovation governance, 35–53. https://doi.org/10.1007/978-3-030-69978-9_4
  • Deng, B. (2015). Machine ethics: The robot’s dilemma. Nature, 523(7558), 24–26. https://doi.org/10.1038/523024a
  • Shostack, A. (2014). Threat modeling: Designing for security. http://ci.nii.ac.jp/ncid/BB16065709
  • Barocas, S., & Selbst, A. D. (2016). Big data’s disparate impact. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2477899
  • Brill, T. M. (2018). Siri, Alexa, and Other Digital Assistants: A study of Customer Satisfaction with Artificial intelligence applications. In University of Dallas, Electronic Dissertations & Theses. https://core.ac.uk/download/pdf/236409258.pdf
  • Bostrom, N., & Yudkowsky, E. (2011). The Ethics of artificial Intelligence (William Ramsey & Keith Frankish, Eds.). https://nickbostrom.com/ethics/artificial-intelligence.pdf
  • Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., & Vayena, E. (2018). AI4People—An Ethical Framework for a Good AI Society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5
  • Sorbie, A. (2019). Medical data donation, consent and the public interest after death: a gateway to posthumous data use. In Philosophical studies series, 115–130. https://doi.org/10.1007/978-3-030-04363-6_7
  • Yu, S., Carroll, F., & Bentley, B. L. (2024). Insights into Privacy Protection research in AI. IEEE Access, 12, 41704–41726. https://doi.org/10.1109/access.2024.3378126
  • Das, B. C., Amini, M. H., & Wu, Y. (2025). Security and privacy Challenges of large language Models: a survey. ACM Computing Surveys. https://doi.org/10.1145/3712001
  • Liu, Y., Zhang, Z., & Wu, Y. (2024). Will generative AI create a new social divide? Investigating the impacts of generative AI use on social capital in China. International Journal of Human-Computer Interaction, 1–17. https://doi.org/10.1080/10447318.2024.2443242
  • Sengar, S. S., Hasan, A. B., Kumar, S., & Carroll, F. (2024). Generative artificial intelligence: a systematic review and applications. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-024-20016-1
  • Gendron, J., & Maduro, R. (2023). The role of inference in AI: Start S.M.A.L.L. with mindful modeling. In Elsevier eBooks, 185–229. https://doi.org/10.1016/b978-0-32-391919-7.00019-6
  • Meghraoui, K., Sebari, I., Bensiali, S., & Ait El Kadi, K. (2022). On behalf of an intelligent approach based on 3D CNN and multimodal remote sensing data for precise crop yield estimation: Case study of wheat in Morocco. Advanced Engineering Science, 2, 118–126. Retrieved from https://publish.mersin.edu.tr/index.php/ades/article/view/329
  • Meghraoui, K., Sebari, I., Bensiali, S., & Ait El Kadi, K. (2022). On behalf of an intelligent approach based on 3D CNN and multimodal remote sensing data for precise crop yield estimation: Case study of wheat in Morocco. Advanced Engineering Science, 2, 118–126. Retrieved from https://publish.mersin.edu.tr/index.php/ades/article/view/329
  • Ertuğrul, Özgür L. ., & İnal, F. . (2022). Assessment of the artificial fiber contribution on the shear strength parameters of soils. Advanced Engineering Science, 2, 93–100. Retrieved from https://publish.mersin.edu.tr/index.php/ades/article/view/172
  • Kayıran, H. F. (2022). The function of artificial intelligence and its sub-branches in the field of health. Engineering Applications, 1(2), 99–107. Retrieved from https://publish.mersin.edu.tr/index.php/enap/article/view/328
  • Basholli, F. ., Mema , B. ., & Basholli , A. . (2024). Training of information technology personnel through simulations for protection against cyber attacks. Engineering Applications, 3(1), 45–58. Retrieved from https://publish.mersin.edu.tr/index.php/enap/article/view/1191
  • Kayıran, H. F., & Şahmeran, U. (2022). Development of individualized education system with artificial intelligence Fuzzy logic method . Engineering Applications, 1(2), 137–144. Retrieved from https://publish.mersin.edu.tr/index.php/enap/article/view/677
  • Manduchi, L., Pandey, K., Bamler, R., Cotterell, R., Däubener, S., Fellenz, S., Fischer, A., Gärtner, T., Kirchler, M., Kloft, M., Li, Y., Lippert, C., Gerard, D. M., Nalisnick, E., Ommer, B., Ranganath, R., Rudolph, M., Ullrich, K., Van Den Broeck, G., . . . Fortuin, V. (2024). On the Challenges and Opportunities in Generative AI. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2403.00025
  • Perumal, S., & Devarajan, K. (2025). Comparative Performance Analysis of Machine Learning Algorithms: Random Cut Forest, Robust Random Cut Forest, and Amazon Sage Maker Random Cut Forest for Intrusion Detection Systems Using the CIS IDS 2017 Dataset. Turkish Journal of Engineering, 9(3), 535-543. https://doi.org/10.31127/tuje.1614930
  • Yontar, E. (2023). Challenges, threats and advantages of using blockchain technology in the framework of sustainability of the logistics sector. Turkish Journal of Engineering, 7(3), 186-195. https://doi.org/10.31127/tuje.1094375
  • Sinap, V. (2025). A novel hyperparameter tuning method for enhanced intrusion detection in network security. Turkish Journal of Engineering, 9(3), 519-534. https://doi.org/10.31127/tuje.1624366
  • Yiğit, G. (2025). A Comparative Study of Deep Learning Approaches for Human Action Recognition. Turkish Journal of Engineering, 9(2), 281-289. https://doi.org/10.31127/tuje.1579795
  • Singh, A. (2025). Real Time Intrusion Detection In Edge Computing Using Machine Learning Techniques. Turkish Journal of Engineering, 9(2), 385-393. https://doi.org/10.31127/tuje.1516046

Year 2025, Volume: 9 Issue: 4 , 779 - 790 , 08.10.2025

Kavita Arora , Neha Gupta , Kamal Upreti , Rituraj Jain , G V Radhakrishnan

https://doi.org/10.31127/tuje.1690888
https://izlik.org/JA43AU83CX

Abstract

References

  • Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399. https://doi.org/10.1038/s42256-019-0088-2
  • Campolo, A., Sanfilippo, M. R., Whittaker, M., & Crawford, K. (2017). AI Now 2017 Report. In https://ainowinstitute.org/wp-content/uploads/2023/04/AI_Now_2018_Report.pdf. https://www.microsoft.com/en-us/research/uploads/prod/2018/02/AI_Now_2017_Report.pdf
  • Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on Bias and Fairness in Machine Learning. ACM Computing Surveys, 54(6), 1–35. https://doi.org/10.1145/3457607
  • Kumar, G. S., Premalatha, K., Maheshwari, G. U., Kanna, P. R., Vijaya, G., & Nivaashini, M. (2023). Differential privacy scheme using Laplace mechanism and statistical method computation in deep neural network for privacy preservation. Engineering Applications of Artificial Intelligence, 128, 107399. https://doi.org/10.1016/j.engappai.2023.107399
  • Goncalves, M., Hu, Y., Aliagas, I., & Cerdá, L. M. (2024). Neuromarketing algorithms’ consumer privacy and ethical considerations: challenges and opportunities. Cogent Business & Management, 11(1). https://doi.org/10.1080/23311975.2024.2333063
  • Liu, Q., Shakya, R., Khalil, M., & Jovanovic, J. (2025). Advancing privacy in learning analytics using differential privacy. In LAK ’25: Proceedings of the 15th International Learning Analytics and Knowledge Conference, 181–191. https://doi.org/10.1145/3706468.3706493
  • Dignum, V. (2018). Ethics in artificial intelligence: introduction to the special issue. Ethics and Information Technology, 20(1), 1–3. https://doi.org/10.1007/s10676-018-9450-z
  • Chaudhuri, K., Guo, C., Rabbat, M., & Meta AI. (2022). Privacy-Aware compression for federated data analysis. In Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence, PMLR 180:296–306. https://proceedings.mlr.press/v180/chaudhuri22a/chaudhuri22a.pdf
  • Stahl, B. C. (2021). Ethical issues of AI. In SpringerBriefs in research and innovation governance, 35–53. https://doi.org/10.1007/978-3-030-69978-9_4
  • Deng, B. (2015). Machine ethics: The robot’s dilemma. Nature, 523(7558), 24–26. https://doi.org/10.1038/523024a
  • Shostack, A. (2014). Threat modeling: Designing for security. http://ci.nii.ac.jp/ncid/BB16065709
  • Barocas, S., & Selbst, A. D. (2016). Big data’s disparate impact. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2477899
  • Brill, T. M. (2018). Siri, Alexa, and Other Digital Assistants: A study of Customer Satisfaction with Artificial intelligence applications. In University of Dallas, Electronic Dissertations & Theses. https://core.ac.uk/download/pdf/236409258.pdf
  • Bostrom, N., & Yudkowsky, E. (2011). The Ethics of artificial Intelligence (William Ramsey & Keith Frankish, Eds.). https://nickbostrom.com/ethics/artificial-intelligence.pdf
  • Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., & Vayena, E. (2018). AI4People—An Ethical Framework for a Good AI Society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5
  • Sorbie, A. (2019). Medical data donation, consent and the public interest after death: a gateway to posthumous data use. In Philosophical studies series, 115–130. https://doi.org/10.1007/978-3-030-04363-6_7
  • Yu, S., Carroll, F., & Bentley, B. L. (2024). Insights into Privacy Protection research in AI. IEEE Access, 12, 41704–41726. https://doi.org/10.1109/access.2024.3378126
  • Das, B. C., Amini, M. H., & Wu, Y. (2025). Security and privacy Challenges of large language Models: a survey. ACM Computing Surveys. https://doi.org/10.1145/3712001
  • Liu, Y., Zhang, Z., & Wu, Y. (2024). Will generative AI create a new social divide? Investigating the impacts of generative AI use on social capital in China. International Journal of Human-Computer Interaction, 1–17. https://doi.org/10.1080/10447318.2024.2443242
  • Sengar, S. S., Hasan, A. B., Kumar, S., & Carroll, F. (2024). Generative artificial intelligence: a systematic review and applications. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-024-20016-1
  • Gendron, J., & Maduro, R. (2023). The role of inference in AI: Start S.M.A.L.L. with mindful modeling. In Elsevier eBooks, 185–229. https://doi.org/10.1016/b978-0-32-391919-7.00019-6
  • Meghraoui, K., Sebari, I., Bensiali, S., & Ait El Kadi, K. (2022). On behalf of an intelligent approach based on 3D CNN and multimodal remote sensing data for precise crop yield estimation: Case study of wheat in Morocco. Advanced Engineering Science, 2, 118–126. Retrieved from https://publish.mersin.edu.tr/index.php/ades/article/view/329
  • Meghraoui, K., Sebari, I., Bensiali, S., & Ait El Kadi, K. (2022). On behalf of an intelligent approach based on 3D CNN and multimodal remote sensing data for precise crop yield estimation: Case study of wheat in Morocco. Advanced Engineering Science, 2, 118–126. Retrieved from https://publish.mersin.edu.tr/index.php/ades/article/view/329
  • Ertuğrul, Özgür L. ., & İnal, F. . (2022). Assessment of the artificial fiber contribution on the shear strength parameters of soils. Advanced Engineering Science, 2, 93–100. Retrieved from https://publish.mersin.edu.tr/index.php/ades/article/view/172
  • Kayıran, H. F. (2022). The function of artificial intelligence and its sub-branches in the field of health. Engineering Applications, 1(2), 99–107. Retrieved from https://publish.mersin.edu.tr/index.php/enap/article/view/328
  • Basholli, F. ., Mema , B. ., & Basholli , A. . (2024). Training of information technology personnel through simulations for protection against cyber attacks. Engineering Applications, 3(1), 45–58. Retrieved from https://publish.mersin.edu.tr/index.php/enap/article/view/1191
  • Kayıran, H. F., & Şahmeran, U. (2022). Development of individualized education system with artificial intelligence Fuzzy logic method . Engineering Applications, 1(2), 137–144. Retrieved from https://publish.mersin.edu.tr/index.php/enap/article/view/677
  • Manduchi, L., Pandey, K., Bamler, R., Cotterell, R., Däubener, S., Fellenz, S., Fischer, A., Gärtner, T., Kirchler, M., Kloft, M., Li, Y., Lippert, C., Gerard, D. M., Nalisnick, E., Ommer, B., Ranganath, R., Rudolph, M., Ullrich, K., Van Den Broeck, G., . . . Fortuin, V. (2024). On the Challenges and Opportunities in Generative AI. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2403.00025
  • Perumal, S., & Devarajan, K. (2025). Comparative Performance Analysis of Machine Learning Algorithms: Random Cut Forest, Robust Random Cut Forest, and Amazon Sage Maker Random Cut Forest for Intrusion Detection Systems Using the CIS IDS 2017 Dataset. Turkish Journal of Engineering, 9(3), 535-543. https://doi.org/10.31127/tuje.1614930
  • Yontar, E. (2023). Challenges, threats and advantages of using blockchain technology in the framework of sustainability of the logistics sector. Turkish Journal of Engineering, 7(3), 186-195. https://doi.org/10.31127/tuje.1094375
  • Sinap, V. (2025). A novel hyperparameter tuning method for enhanced intrusion detection in network security. Turkish Journal of Engineering, 9(3), 519-534. https://doi.org/10.31127/tuje.1624366
  • Yiğit, G. (2025). A Comparative Study of Deep Learning Approaches for Human Action Recognition. Turkish Journal of Engineering, 9(2), 281-289. https://doi.org/10.31127/tuje.1579795
  • Singh, A. (2025). Real Time Intrusion Detection In Edge Computing Using Machine Learning Techniques. Turkish Journal of Engineering, 9(2), 385-393. https://doi.org/10.31127/tuje.1516046
There are 33 citations in total.

Details

Primary Language English
Subjects Information Systems (Other)
Journal Section Research Article
Authors

Kavita Arora 0000-0002-3389-3939

Neha Gupta 0000-0003-0905-5457

Kamal Upreti 0000-0003-0665-530X

Rituraj Jain 0000-0002-5532-1245

G V Radhakrishnan 0000-0003-4046-0995

Submission Date May 4, 2025
Acceptance Date June 26, 2025
Publication Date October 8, 2025
DOI https://doi.org/10.31127/tuje.1690888
IZ https://izlik.org/JA43AU83CX
Published in Issue Year 2025 Volume: 9 Issue: 4

Cite

APA Arora, K., Gupta, N., Upreti, K., Jain, R., & Radhakrishnan, G. V. (2025). Protecting Privacy and Ethics in AI-driven Conversational Systems using Laplace Mechanism. Turkish Journal of Engineering, 9(4), 779-790. https://doi.org/10.31127/tuje.1690888
AMA 1.Arora K, Gupta N, Upreti K, Jain R, Radhakrishnan GV. Protecting Privacy and Ethics in AI-driven Conversational Systems using Laplace Mechanism. TUJE. 2025;9(4):779-790. doi:10.31127/tuje.1690888
Chicago Arora, Kavita, Neha Gupta, Kamal Upreti, Rituraj Jain, and G V Radhakrishnan. 2025. “Protecting Privacy and Ethics in AI-Driven Conversational Systems Using Laplace Mechanism”. Turkish Journal of Engineering 9 (4): 779-90. https://doi.org/10.31127/tuje.1690888.
EndNote Arora K, Gupta N, Upreti K, Jain R, Radhakrishnan GV (October 1, 2025) Protecting Privacy and Ethics in AI-driven Conversational Systems using Laplace Mechanism. Turkish Journal of Engineering 9 4 779–790.
IEEE [1]K. Arora, N. Gupta, K. Upreti, R. Jain, and G. V. Radhakrishnan, “Protecting Privacy and Ethics in AI-driven Conversational Systems using Laplace Mechanism”, TUJE, vol. 9, no. 4, pp. 779–790, Oct. 2025, doi: 10.31127/tuje.1690888.
ISNAD Arora, Kavita - Gupta, Neha - Upreti, Kamal - Jain, Rituraj - Radhakrishnan, G V. “Protecting Privacy and Ethics in AI-Driven Conversational Systems Using Laplace Mechanism”. Turkish Journal of Engineering 9/4 (October 1, 2025): 779-790. https://doi.org/10.31127/tuje.1690888.
JAMA 1.Arora K, Gupta N, Upreti K, Jain R, Radhakrishnan GV. Protecting Privacy and Ethics in AI-driven Conversational Systems using Laplace Mechanism. TUJE. 2025;9:779–790.
MLA Arora, Kavita, et al. “Protecting Privacy and Ethics in AI-Driven Conversational Systems Using Laplace Mechanism”. Turkish Journal of Engineering, vol. 9, no. 4, Oct. 2025, pp. 779-90, doi:10.31127/tuje.1690888.
Vancouver 1.Kavita Arora, Neha Gupta, Kamal Upreti, Rituraj Jain, G V Radhakrishnan. Protecting Privacy and Ethics in AI-driven Conversational Systems using Laplace Mechanism. TUJE. 2025 Oct. 1;9(4):779-90. doi:10.31127/tuje.1690888
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