Arseam

International Journal of Advances in Engineering & Scientific Research

PRIVACY ENGINEERING AND ETHICAL CHALLENGES IN EMERGING TECHNOLOGIES: SOLUTIONS FO

Shaurya Jain

Shaurya Jain Software Engineering & Global Privacy Expert

DOI : https://doi.org/10.53882/IJAESR.2024.1101005 Page No : 65-82

Published Online : 2024-06-30

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As Industry 5.0 is an unfolding phase, it promises a transformative phase in the spectrum of digital transformation for this industry, with its impetus on mass customization to improve production efficiency in integrating advanced innovations such as analytics in the cloud, visualization in data, self-healing networks, automated robots, and universal machines. These technologies can achieve capabilities beyond the limitations of Industry 4.0, but its development must be controlled with constraints of legal and ethical guidance. As these technologies advance, so do the issues surrounding privacy and data protection. There are still developing ethical standards and differences in regulatory frameworks across various regions. The lack of standard ethical standards poses challenges that can lead to privacy violations, data breaches, and other ethical dilemmas that have to be carefully navigated. The present paper explores the ethical impact of emerging technologies in Industry 5.0, in the domain of privacy engineering, which is discussed and potential solutions given to the legal and regulatory challenges with its implementation. It explains how privacy engineering can contribute to risk mitigation while aligning with global standards, protecting user data in this fast-changing technological environment.

Keywords: privacy; ethics; cyber security; regulations; standards; emerging technologies.

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Formosa, P. (2023). A principlist-based study of the ethical design and acceptability of artificial social agents. Int. J. Hum. Comput. Stud. 2023, 172, 102980. DOI: https://doi.org/10.1016/j.ijhcs.2022.102980
GDPR. (2022). Complete Guide to GDPR Compliance. Retrieved From: https://gdpr.eu (accessed on 12 December 2022).
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Hansson, S.O. (2020). How extreme is the precautionary principle. Nanoethics 2020, 14, 245–257. Retrieved From: https://link.springer.com/article/10.1007/s11569-020-00373-5
H-Cloud (2020). Cloud Manufacturing and Social Software Based Context Sensitive Product-Service Engineering Environment for Globally Distributed Enterprise. Retrieved From: https://cordis.europa.eu/project/id/636692
Kendal, E. (2022). Ethical, Legal and Social Implications of Emerging Technology (ELSIET) Symposium. J. Bioeth. Inq. 2022, 19, 363–370. DOI: https://doi.org/10.1007/s11673-022-10197-5
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Laxminarayana Korada, et. al (2024). Artificial Intelligence On The Administration Of Financial Markets. DOI: https://doi.org/10.62441/nano-ntp.vi.2493
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Lubna Luxmi Dhirani (2022). Ethical Dilemmas and Privacy Issues in Emerging Technologies: A Review. DOI: https://doi.org/10.3390/s23031151
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Retrieved From: file:///C:/Users/LENOVO/Downloads/214-Article%20Text-1166-892-10-20221211.pdf
Siau, K.; Wang, W. (2020). Artificial intelligence (AI) Ethics: Ethics of AI and ethical AI. J. Database Manag. 2020, 31, 74–87. Retrieved From: https://www.igi-global.com/article/artificial-intelligence-ai-ethics/249172
Singh, S. K., Choudhary, S. K., Ranjan, P., & Dahiya, S. (2022). Comparative Analysis of Machine Learning Models and Data Analytics Techniques for Fraud Detection in Banking System. International Journal of Core Engineering & Management, 7(1), 64. ISSN 2348-9510. Retrieved From: https://www.researchgate.net/publication/384678305_COMPARATIVE_ANALYSIS_OF_MACHINE_LEARNING_MODELS_AND_DATA_ANALYTICS_TECHNIQUES_FOR_FRAUD_DETECTION_IN_BANKING_SYSTEM
Sujithra, M., Velvadivu, P., Rathika, J., Priyadharshini, R., & Preethi, P. (2022, October). A Study On Psychological Stress Of Working Women In Educational Institution Using Machine Learning. In 2022 13th. International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-7). IEEE. Retrieved From: https://ieeexplore.ieee.org/document/9984460
Suresh, K., Reddy, P. P., & Preethi, P. (2019). A novel key exchange algorithm for security in internet of things. Indones. J. Electr. Eng. Comput. Sci, 16(3), 1515-1520. DOI: http://dx.doi.org/10.11591/ijeecs.v16.i3.pp1515-1520
Umbrello, S.; Van de Poel, I. (2021). Mapping value sensitive design onto AI for social good principles. AI Ethics 2021, 1, 283–296. DOI: https://doi.org/10.1007/s43681-021-00038-3
Urfa Khairatun Hisan Muhammad Miftahul Amri (2022). Artificial Intelligence for Human Life: A Critical Opinion from Medical Bioethics Perspective (accessed on 20 September 2022).
Vidgen, R.; Shaw, S.; Grant, D.B. (2017). Management challenges in creating value from business analytics. Eur. J. Oper. Res. 2017, 261, 626–639. DOI: https://doi.org/10.1016/j.ejor.2017.02.023
Vigliarolo, B. (2021). 85% of Organizations Will Be ‘Cloud-First’ by 2025, Says Gartner. 2021. Available online: https://www.techrepublic. com/article/85-of-organizations-will-be-cloud-first-by-2025-says-gartner (accessed on 30 November 2022). Retrieved From: https://www.techrepublic.com/article/85-of-organizations-will-be-cloud-first-by-2025-says-gartner/
Wan, Z. (2022). Sociotechnical safeguards for genomic data privacy. Nat. Rev. Genet. 2022, 23, 429– 445. [PubMed]. Retrieved From: https://doi.org/10.1038/s41576-022-00455-y

Jain S. (2024), Privacy Engineering And Ethical Challenges In Emerging Technologies: Solutions Fo”, International Journal of Advances in Engineering & Scientific Research, Volume 11, Issue 1, 2024, pp 65-82. DOI : https://doi.org/10.53882/IJAESR.2024.1101005

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REFERENCES

Anguraju, K., Kumar, N. S., Kumar, S. J., Anandhan, K., & Preethi, P. (2020). Adaptive feature selection based learning model for emotion recognition. J Critic Rev. Retrieved From: https://scholar.google.com/citations?user=D9KJ09wAAAAJ&hl=en
Brey, P. (2017). Ethics of Emerging Technology. In The Ethics of Technology Methods and Approaches; Rowan & Littlefield International: Lanham, MD, USA, 2017. Retrieved From: https://research.utwente.nl/en/publications/ethics-of-emerging-technologies
Choudhary, S. K., Ranjan, P., Dahiya, S., & Singh, S. K. (2023). Detecting Malware Attacks Based on Machine Learning Techniques for Improve Cyber security. International Journal of Core Engineering & Management, 7(8), 88. ISSN 2348-9510. Retrieved From: https://ijcem.in/wp-content/uploads/DETECTING-MALWARE-ATTACKS-BASED-ON-MACHINE-LEARNING-TECHNIQUES-FOR-IMPROVE-CYBERSECURITY.pdf
Christophe Olivier Schneble et. al (2020). Google's Project Nightingale highlights the necessity of data science ethics review. DOI: https://doi.org/10.15252/emmm.202012053
Dahiya, S., Singh, S. K., Choudhary, S. K., & Ranjan, P. (2022). Fundamentals of Digital Transformation in Financial Services: Key Drivers and Strategies. International Journal of Core Engineering & Management, 7(3), 41. ISSN 2348-9510. Retrieved From: https://www.researchgate.net/publication/384678363_FUNDAMENTALS_OF_DIGITAL_TRANSFORMATION_IN_FINANCIAL_SERVICES_KEY_DRIVERS_AND_STRATEGIES
Formosa, P. (2023). A principlist-based study of the ethical design and acceptability of artificial social agents. Int. J. Hum. Comput. Stud. 2023, 172, 102980. DOI: https://doi.org/10.1016/j.ijhcs.2022.102980
GDPR. (2022). Complete Guide to GDPR Compliance. Retrieved From: https://gdpr.eu (accessed on 12 December 2022).
Grunwald, A. (2020). The objects of technology assessment. Hermeneutic extension of consequentialist reasoning. J. Responsible Innov. 2020, 7, 96–112. DOI: https://doi.org/10.1080/23299460.2019.1647086
Hansson, S.O. (2020). How extreme is the precautionary principle. Nanoethics 2020, 14, 245–257. Retrieved From: https://link.springer.com/article/10.1007/s11569-020-00373-5
H-Cloud (2020). Cloud Manufacturing and Social Software Based Context Sensitive Product-Service Engineering Environment for Globally Distributed Enterprise. Retrieved From: https://cordis.europa.eu/project/id/636692
Kendal, E. (2022). Ethical, Legal and Social Implications of Emerging Technology (ELSIET) Symposium. J. Bioeth. Inq. 2022, 19, 363–370. DOI: https://doi.org/10.1007/s11673-022-10197-5
Korada, L. (2024). Data Poisoning-What Is It and How It Is Being Addressed by the Leading Gen AI Providers. European Journal of Advances in Engineering and Technology, 11(5), 105-109. DOI: http://dx.doi.org/10.5281/zenodo.13318796
Laxminarayana Korada, et. al (2024). Artificial Intelligence On The Administration Of Financial Markets. DOI: https://doi.org/10.62441/nano-ntp.vi.2493
Liu, J.; Li, J.; Li, W.; Wu, J.(2016). Rethinking big data: A review on the data quality and usage issues. ISPRS J. Photogram. Remote Sens. 2016, 115, 134–142. DOI: https://doi.org/10.1016/j.isprsjprs.2015.11.006
Lubna Luxmi Dhirani (2022). Ethical Dilemmas and Privacy Issues in Emerging Technologies: A Review. DOI: https://doi.org/10.3390/s23031151
Lynn, T. et. al (2021). Data Privacy and Trust in Cloud Computing; Palgrave Macmillan: London, UK, 2021. Retrieved From: https://library.oapen.org/handle/20.500.12657/42922
Ranjan, P., Dahiya, S., Singh, S. K., & Choudhary, S. K. (2023). Enhancing Stock Price Prediction: A Comprehensive Analysis Utilizing Machine Learning and Deep Learning Approaches. International Journal of Core Engineering & Management, 7(5), 146. ISSN 2348-9510. Retrieved From: https://ijcem.in/wp-content/uploads/ENHANCING-STOCK-PRICE-PREDICTION-A-COMPREHENSIVE-ANALYSIS-UTILIZING-MACHINE-LEARNING-AND-DEEP-LEARNING-APPROACHES.pdf
Reiss, M. (2010). Ethical Thinking. In Ethics in the Science and Technology Classroom; Brill: Leiden, the Netherlands, 2010. Retrieved From: https://brill.com/display/title/36978?language=en
Rekha, P., Saranya, T., Preethi, P., Saraswathi, L., & Shobana, G. (2017). Smart Agro Using Arduino and GSM. International Journal of Emerging Technologies in Engineering Research (IJETER) Volume, 5. Retrieved From: https://www.ijeter.everscience.org/Manuscripts/Volume-5/Issue-3/Vol-5-issue-3-M-09.pdf
Retrieved From: file:///C:/Users/LENOVO/Downloads/214-Article%20Text-1166-892-10-20221211.pdf
Siau, K.; Wang, W. (2020). Artificial intelligence (AI) Ethics: Ethics of AI and ethical AI. J. Database Manag. 2020, 31, 74–87. Retrieved From: https://www.igi-global.com/article/artificial-intelligence-ai-ethics/249172
Singh, S. K., Choudhary, S. K., Ranjan, P., & Dahiya, S. (2022). Comparative Analysis of Machine Learning Models and Data Analytics Techniques for Fraud Detection in Banking System. International Journal of Core Engineering & Management, 7(1), 64. ISSN 2348-9510. Retrieved From: https://www.researchgate.net/publication/384678305_COMPARATIVE_ANALYSIS_OF_MACHINE_LEARNING_MODELS_AND_DATA_ANALYTICS_TECHNIQUES_FOR_FRAUD_DETECTION_IN_BANKING_SYSTEM
Sujithra, M., Velvadivu, P., Rathika, J., Priyadharshini, R., & Preethi, P. (2022, October). A Study On Psychological Stress Of Working Women In Educational Institution Using Machine Learning. In 2022 13th. International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-7). IEEE. Retrieved From: https://ieeexplore.ieee.org/document/9984460
Suresh, K., Reddy, P. P., & Preethi, P. (2019). A novel key exchange algorithm for security in internet of things. Indones. J. Electr. Eng. Comput. Sci, 16(3), 1515-1520. DOI: http://dx.doi.org/10.11591/ijeecs.v16.i3.pp1515-1520
Umbrello, S.; Van de Poel, I. (2021). Mapping value sensitive design onto AI for social good principles. AI Ethics 2021, 1, 283–296. DOI: https://doi.org/10.1007/s43681-021-00038-3
Urfa Khairatun Hisan Muhammad Miftahul Amri (2022). Artificial Intelligence for Human Life: A Critical Opinion from Medical Bioethics Perspective (accessed on 20 September 2022).
Vidgen, R.; Shaw, S.; Grant, D.B. (2017). Management challenges in creating value from business analytics. Eur. J. Oper. Res. 2017, 261, 626–639. DOI: https://doi.org/10.1016/j.ejor.2017.02.023
Vigliarolo, B. (2021). 85% of Organizations Will Be ‘Cloud-First’ by 2025, Says Gartner. 2021. Available online: https://www.techrepublic. com/article/85-of-organizations-will-be-cloud-first-by-2025-says-gartner (accessed on 30 November 2022). Retrieved From: https://www.techrepublic.com/article/85-of-organizations-will-be-cloud-first-by-2025-says-gartner/
Wan, Z. (2022). Sociotechnical safeguards for genomic data privacy. Nat. Rev. Genet. 2022, 23, 429– 445. [PubMed]. Retrieved From: https://doi.org/10.1038/s41576-022-00455-y

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