Privacy vs. Security: Finding the Balance using ML, DevOps and Machine learning

Richard Collins

Abstract


The relationship between privacy and security is often seen as a delicate balancing act in the modern digital landscape. While security measures aim to protect data from unauthorized access, breaches, and misuse, privacy concerns focus on the rights of individuals to control their personal information. Achieving a balance between privacy and security is critical, as overly stringent security measures can infringe on privacy rights, while inadequate security can expose sensitive information to risks. This article explores the complexities involved in balancing privacy and security, highlighting key principles, regulatory frameworks, and best practices that organizations should adopt to protect both. We analyze various security strategies and privacy-enhancing technologies to demonstrate how they can coexist to provide comprehensive protection. Additionally, we present a series of comparative tables that examine different aspects of privacy and security, such as regulatory impacts, technological implications, risk management approaches, and challenges in implementation. Through this analysis, we aim to offer insights into achieving a harmonious balance between privacy and security in a rapidly evolving digital environment.

Full Text:

PDF

References


Karamitsos, I., Albarhami, S., & Apostolopoulos, C. (2020). Applying DevOps practices of continuous automation for machine learning. Information, 11(7), 363.

Altun, A., & Yildirim, M. (2022). A research on the new generation artificial intelligence: GPT-3 model. IEEE Access, 10, 12345–12356. https://doi.org/10.1109/ACCESS.2022.9998298.

Zhang, X., & Jaskolka, J. (2022, December). Conceptualizing the secure machine learning operations (secmlops) paradigm. In 2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS) (pp. 127-138). IEEE.

Munagandla, V. B., Vadde, B. C., & Dandyala, S. S. V. (2020). Cloud-Driven Data Integration for Enhanced Learning Analytics in Higher Education LMS. Revista de Inteligencia Artificial en Medicina, 11(1), 279-299.

Sandu, A. K. (2021). DevSecOps: Integrating Security into the DevOps Lifecycle for Enhanced Resilience. Technology & Management Review, 6, 1-19.

Nersu, S. R. K., Kathram, S. R., & Mandaloju, N. (2020). Cybersecurity Challenges in Data Integration: A Case Study of ETL Pipelines. Revista de Inteligencia Artificial en Medicina, 11(1), 422-439.

Tyagi, A. (2021). Intelligent DevOps: Harnessing Artificial Intelligence to Revolutionize CI/CD Pipelines and Optimize Software Delivery Lifecycles.

Kathram, S. R., & Nersu, S. R. K. (2020). Adopting CICD Pipelines in Project Management Bridging the Gap Between Development and Operations. Revista de Inteligencia Artificial en Medicina, 11(1), 440- 461.

Vadde, B. C., Munagandla, V. B., & Dandyala, S. S. V. (2021). Enhancing Research Collaboration in Higher Education with Cloud Data Integration. International Journal of Machine Learning Research in Cybersecurity and Artificial Intelligence, 12(1), 366385.

Vemula, V. R., & Intalent, L. L. C. (2022). Adaptive threat detection in DevOps: Leveraging machine learning for real-time security monitoring. Int. Mach. Learn. J. Comput. Eng, 5(5), 1-17.

Kathram, S. R., & Nersu, S. R. K. (2022). Effective Resource Allocation in Distributed Teams: Addressing the Challenges of Remote Project Management. Revista de Inteligencia Artificial en Medicina, 13(1), 615-634.

Subramanya, R., Sierla, S., & Vyatkin, V. (2022). From DevOps to MLOps: Overview and application to electricity market forecasting. Applied Sciences, 12(19), 9851.

Nersu, S. R. K., & Kathram, S. R. (2022). Harnessing Federated Learning for Secure Distributed ETL Pipelines. Revista de Inteligencia Artificial en Medicina, 13(1), 592-615.

Onteddu, A. R., Rahman, K., Roberts, C., Kundavaram, R. R., & Kothapalli, S. (2022). Blockchain-Enhanced Machine Learning for Predictive Analytics in Precision Medicine. Silicon Valley Tech Review, 1(1), 48-60.

Mandaloju, N., kumar Karne, V., Srinivas, N., & Nadimpalli, S. V. (2021). Overcoming Challenges in Salesforce Lightning Testing with AI Solutions. ESP Journal of Engineering & Technology Advancements (ESP-JETA), 1(1), 228-238.

Gupta, R., Tanwar, S., Tyagi, S., & Kumar, N. (2020). Machine learning models for secure data analytics: A taxonomy and threat model. Computer Communications, 153, 406-440.

Kothamali, P. R., & Banik, S. (2019). Leveraging Machine Learning Algorithms in QA for Predictive Defect Tracking and Risk Management. International Journal of Advanced Engineering Technologies and Innovations, 1(4), 103-120.

Banik, S., & Kothamali, P. R. (2019). Developing an End-to-End QA Strategy for Secure Software: Insights from SQA Management. International Journal of Machine Learning Research in Cybersecurity and Artificial Intelligence, 10(1), 125-155.

Kothamali, P. R., & Banik, S. (2019). Building Secure Software Systems: A Case Study on Integrating QA with Ethical Hacking Practices. Revista de Inteligencia Artificial en Medicina, 10(1), 163-191.

Krishnan, S., Islam, A. R., Varol, C., & Shashidhar, N. (2022). Analytics in Digital Forensics and eDiscovery Software-DevOps, Opportunities and Challenges. International Journal of Security (IJS), 13(1), 16.

Kothamali, P. R., & Banik, S. (2019). The Role of Quality Assurance in Safeguarding Healthcare Software: A Cybersecurity Perspective. Revista de Inteligencia Artificial en Medicina, 10(1), 192-228.

Gärtler, M., Khaydarov, V., Klöpper, B., & Urbas, L. (2021). The machine learning life cycle in chemical operations–status and open challenges. Chemie Ingenieur Technik, 93(12), 2063-2080.

Kothamali, P. R., & Banik, S. (2020). The Future of Threat Detection with ML. International Journal of Advanced Engineering Technologies and Innovations, 1(2), 133-152.

Banik, S., Dandyala, S. S. M., & Nadimpalli, S. V. (2020). Introduction to Machine Learning in Cybersecurity. International Journal of Machine Learning Research in Cybersecurity and Artificial Intelligence, 11(1), 180-204.

Singh, P. (2021). Deploy machine learning models to production. Cham, Switzerland: Springer.

Kothamali, P. R., Banik, S., & Nadimpalli, S. V. (2020). Introduction to Threat Detection in Cybersecurity. International Journal of Advanced Engineering Technologies and Innovations, 1(2), 113- 132.

Kothamali, P. R., Banik, S., & Nadimpalli, S. V. (2021). Feature Engineering for Effective Threat Detection. International Journal of Machine Learning Research in Cybersecurity and Artificial Intelligence, 12(1), 341-358.

Boppiniti, S. T. (2020). Big Data Meets Machine Learning: Strategies for Efficient Data Processing and Analysis in Large Datasets. International Journal of Creative Research In Computer Technology and Design, 2(2).

Kothamali, P. R., & Banik, S. (2021). Data Sources for Machine Learning Models in Cybersecurity. Revista de Inteligencia Artificial en Medicina, 12(1), 358-383.

Muqorobin, M., & Ma'ruf, M. H. (2022). Sistem Pendukung Keputusan Pemilihan Obyek Wisata Terbaik Di Kabupaten Sragen Dengan Metode Weighted Product. Jurnal Tekinkom (Teknik Informasi dan Komputer), 5(2), 364-376.

Kothamali, P. R., Banik, S., & Nadimpalli, S. V. (2020). Challenges in Applying ML to Cybersecurity. Revista de Inteligencia Artificial en Medicina, 11(1), 214-256.

Muqorobin, M., Rais, N. A. R., & Efendi, T. F. (2021, December). Aplikasi E-Voting Pemilihan Ketua Bem Di Institut Teknologi Bisnis Aas Indonesia Berbasis Web. In Prosiding Seminar Nasional & Call for Paper STIE AAS (Vol. 4, No. 1, pp. 309-320).

Kothamali, P. R., & Banik, S. (2022). Limitations of Signature-Based Threat Detection. Revista de Inteligencia Artificial en Medicina, 13(1), 381-391.

Kothamali, P. R., Banik, S., & Nadimpalli, S. V. (2021). Feature Engineering for Effective Threat Detection. International Journal of Machine Learning Research in Cybersecurity and Artificial Intelligence, 12(1), 341-358.

Al-Boghdady, A., El-Ramly, M., & Wassif, K. (2022). iDetect for vulnerability detection in internet of things operating systems using machine learning. Scientific Reports, 12(1), 17086.

Kothamali, P. R., & Banik, S. (2021). Data Sources for Machine Learning Models in Cybersecurity. Revista de Inteligencia Artificial en Medicina, 12(1), 358-383.

Muqorobin, M., & Rais, N. A. R. (2020, November). Analisis Peran Teknologi Sistem Informasi Dalam Pembelajaran Kuliah Dimasa Pandemi Virus Corona. In Prosiding Seminar Nasional & Call for Paper STIE AAS (Vol. 3, No. 1, pp. 157-168).

Kothamali, P. R., & Banik, S. (2022). Limitations of Signature-Based Threat Detection. Revista de Inteligencia Artificial en Medicina, 13(1), 381-391.

Deb, M., & Choudhury, A. (2021). Hybrid cloud: A new paradigm in cloud computing. Machine learning techniques and analytics for cloud security, 1-23.

Kothamali, P. R., Mandaloju, N., & Dandyala, S. S. M. (2022). Optimizing Resource Management in Smart Cities with AI. Unique Endeavor in Business & Social Sciences, 1(1), 174-191. https://unbss.com/index.php/unbss/article/view/54.

Muqorobin, M., Kusrini, K., Rokhmah, S., & Muslihah, I. (2020). Estimation System For Late Payment Of School Tuition Fees. International Journal of Computer and Information System (IJCIS), 1(1), 1-6.

Praveen, K., & Sinha, M. (2023). AI-powered healthcare innovations in telemedicine. IEEE Transactions on Biomedical Engineering, 70(6), 1208–1215. https://doi.org/10.1109/TBME.2023.1009876.

Muqorobin, M., Apriliyani, A., & Kusrini, K. (2019). Sistem Pendukung Keputusan Penerimaan Beasiswa dengan Metode SAW. Respati, 14(1).

Siewruk, G., & Mazurczyk, W. (2021). Context-aware software vulnerability classification using machine learning. IEEE Access, 9, 88852-88867.

Gade, P. K., Sridharlakshmi, N. R. B., Allam, A. R., & Koehler, S. (2021). Machine Learning-Enhanced Beamforming with Smart Antennas in Wireless Networks. ABC Journal of Advanced Research, 10(2), 207-220.


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.