Develop methods for anonymizing data for privacy-preserving AI

Authors

  • Prudhvi Singirikonda Independent Researcher

DOI:

https://doi.org/10.36676/jrps.v15.i1.1545

Keywords:

Privacy-preserving, Anonymization, Cloud Computing, Data Utility, Scalability, Re-identification Attacks

Abstract

This paper looks into the problem of privacy-preserving data publication in cloud computing, which is crucial for preserving the confidentiality of the data and using it for analytical purposes. To address this problem, we have introduced a new heuristic approach to anonymization that enhances data utility while ensuring appropriate levels of data privacy. In our methodology, we apply sophisticated simulation models to evaluate the efficiency of the anonymization method under different data sets. The main results prove that the improvement in the privacy metrics is significant compared to the prior techniques and without making substantial compromises on the usability of the data. Based on these findings, the authors believe that the generalized technique presented in this work could be implemented in genuine cloud computing environments as a powerful tool to address data privacy issues. As the final discussion, this paper presents the real-life significance of implementing this technique and future research recommendations that could expand the scope by experimenting with scalability and more optimization to handle the large volume of data and complications.

References

Aldeen Yousra, S., & Mazleena, S. (2018, May). A new heuristic anonymization technique for privacy preserved datasets publication on cloud computing. In Journal of Physics: Conference Series (Vol. 1003, p. 012030). IOP Publishing. https://iopscience.iop.org/article/10.1088/1742-6596/1003/1/012030/pdf DOI: https://doi.org/10.1088/1742-6596/1003/1/012030

Mallreddy, S. R., & Vasa, Y. (2023). Predictive Maintenance In Cloud Computing And Devops: Ml Models For Anticipating And Preventing System Failures. NVEO-NATURAL VOLATILES & ESSENTIAL OILS Journal| NVEO, 10(1), 213-219. DOI: https://doi.org/10.53555/nveo.v10i1.5751

Mallreddy, S. R., & Vasa, Y. (2023). Natural language querying in SIEM systems: Bridging the gap between security analysts and complex data. NATURAL LANGUAGE QUERYING IN SIEM SYSTEMS: BRIDGING THE GAP BETWEEN SECURITY ANALYSTS AND COMPLEX DATA, 10(1), 205–212. https://doi.org/10.53555/nveo.v10i1.5750 DOI: https://doi.org/10.53555/nveo.v10i1.5750

Vasa, Y., Mallreddy, S. R., & Jami, V. S. (2022). AUTOMATED MACHINE LEARNING FRAMEWORK USING LARGE LANGUAGE MODELS FOR FINANCIAL SECURITY IN CLOUD OBSERVABILITY. International Journal of Research and Analytical Reviews , 9(3), 183–190.

Vasa, Y., Singirikonda, P., & Mallreddy, S. R. (2023). AI Advancements in Finance: How Machine Learning is Revolutionizing Cyber Defense. International Journal of Innovative Research in Science, Engineering and Technology, 12(6), 9051–9060.

Vasa, Y., & Singirikonda, P. (2022). Proactive Cyber Threat Hunting With AI: Predictive And Preventive Strategies. International Journal of Computer Science and Mechatronics, 8(3), 30–36.

Vasa, Y., Mallreddy, S. R., & Jaini, S. (2023). AI And Deep Learning Synergy: Enhancing Real-Time Observability And Fraud Detection In Cloud Environments, 6(4), 36–42. https://doi.org/ 10.13140/RG.2.2.12176.83206

Katikireddi, P. M., Singirikonda, P., & Vasa, Y. (2021). Revolutionizing DEVOPS with Quantum Computing: Accelerating CI/CD pipelines through Advanced Computational Techniques. Innovative Research Thoughts, 7(2), 97–103. https://doi.org/10.36676/irt.v7.i2.1482 DOI: https://doi.org/10.36676/irt.v7.i2.1482

Vasa, Y., Cheemakurthi, S. K. M., & Kilaru, N. B. (2022). Deep Learning Models For Fraud Detection In Modernized Banking Systems Cloud Computing Paradigm. International Journal of Advances in Engineering and Management, 4(6), 2774–2783. https://doi.org/10.35629/5252-040627742783

Vasa, Y., Kilaru, N. B., & Gunnam, V. (2023). Automated Threat Hunting In Finance Next Gen Strategies For Unrivaled Cyber Defense. International Journal of Advances in Engineering and Management, 5(11). https://doi.org/10.35629/5252-0511461470

Vasa, Y., & Mallreddy, S. R. (2022). Biotechnological Approaches To Software Health: Applying Bioinformatics And Machine Learning To Predict And Mitigate System Failures. Natural Volatiles & Essential Oils, 9(1), 13645–13652. https://doi.org/https://doi.org/10.53555/nveo.v9i2.5764

Mallreddy, S. R., & Vasa, Y. (2022). Autonomous Systems In Software Engineering: Reducing Human Error In Continuous Deployment Through Robotics And AI. NVEO - Natural Volatiles & Essential Oils, 9(1), 13653–13660. https://doi.org/https://doi.org/10.53555/nveo.v11i01.5765

Vasa, Y., Jaini, S., & Singirikonda, P. (2021). Design Scalable Data Pipelines For Ai Applications. NVEO - Natural Volatiles & Essential Oils, 8(1), 215–221. https://doi.org/https://doi.org/10.53555/nveo.v8i1.5772 DOI: https://doi.org/10.53555/nveo.v8i1.5772

Singirikonda, P., Jaini, S., & Vasa, Y. (2021). Develop Solutions To Detect And Mitigate Data Quality Issues In ML Models. NVEO - Natural Volatiles & Essential Oils, 8(4), 16968–16973. https://doi.org/https://doi.org/10.53555/nveo.v8i4.5771 DOI: https://doi.org/10.53555/nveo.v8i4.5771

Vasa, Y. (2021). Develop Explainable AI (XAI) Solutions For Data Engineers. NVEO - Natural Volatiles & Essential Oils, 8(3), 425–432. https://doi.org/https://doi.org/10.53555/nveo.v8i3.5769 DOI: https://doi.org/10.53555/nveo.v8i3.5769

Sukender Reddy Mallreddy. (2023). ENHANCING CLOUD DATA PRIVACY THROUGH FEDERATED LEARNING: A DECENTRALIZED APPROACH TO AI MODEL TRAINING. IJRDO -Journal of Computer Science Engineering, 9(8), 15-22. DOI: https://doi.org/10.53555/cse.v9i8.6131

Mallreddy, S.R., Nunnaguppala, L.S.C., & Padamati, J.R. (2022). Ensuring Data Privacy with CRM AI: Investigating Customer Data Handling and Privacy Regulations. ResMilitaris. Vol.12(6). 3789-3799

Nunnagupala, L. S. C. ., Mallreddy, S. R., & Padamati, J. R. . (2022). Achieving PCI Compliance with CRM Systems. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 13(1), 529–535. DOI: https://doi.org/10.61841/turcomat.v13i1.14689

Jangampeta, S., Mallreddy, S.R., & Padamati, J.R. (2021). Anomaly Detection for Data Security in SIEM: Identifying Malicious Activity in Security Logs and User Sessions. 10(12), 295-298

Jangampeta, S., Mallreddy, S. R., & Padamati, J. R. (2021). Data Security: Safeguarding the Digital Lifeline in an Era of Growing Threats. International Journal for Innovative Engineering and Management Research, 10(4), 630-632.

Sukender Reddy Mallreddy(2020).Cloud Data Security: Identifying Challenges and Implementing Solutions.JournalforEducators,TeachersandTrainers,Vol.11(1).96 -102.

Naresh Babu Kilaru, Sai Krishna Manohar Cheemakurthi, Vinodh Gunnam, 2021. "SOAR Solutions in PCI Compliance: Orchestrating Incident Response for Regulatory Security"ESP Journal of Engineering & Technology Advancements 1(2): 78-84. : 10.56472/25832646/ESP-V1I2P111

Sayyaparaju, K. K., Nunnaguppala, L. S. C. , & Padamati, J. R.. (2023). "Unlocking SIEM Potential: Secure, Scalable Cloud Architecture with Artificial Intelligence Machine Learning", International Journal For Recent Development In Science And Technology, 7(03), 117-130

Nunnaguppala, L. S. C. . (2023). "A Future-Proof Approach To Cybersecurity Compliance: The Power Of AI And ML In SIEM, SOAR, And Cloud SOC", Res Militaris, 13(4), 1469–1480

Sayyaparaju, K. K., Nunnaguppala, L. S. C. , & Padamati, J. R.. (2021). "Building SecureAI/ML Pipelines: Cloud Data Engineeringfor Compliance and Vulnerability Management", International Journal for Innovative Engineering and Management Research,10(10), 330-340

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Published

30-01-2024

How to Cite

Prudhvi Singirikonda. (2024). Develop methods for anonymizing data for privacy-preserving AI. International Journal for Research Publication and Seminar, 15(1), 224–232. https://doi.org/10.36676/jrps.v15.i1.1545