BIG DATA PRIVACY AND SECURITY USING CONTINUOUS DATA PROTECTION DATA OBLIVIOUSNESS METHODOLOGIES

Authors

  • Swapna Narla

DOI:

https://doi.org/10.46243/jst.2022.v7.i02.pp423-436

Keywords:

Big Data Privacy,, Data Security,, Continuous Data Protection (CDP),, Data Obliviousness, Homomorphic Encryption,, Secure Multiparty Computation (SMC)

Abstract

Large databases must be protected from breaches, unauthorised access, and misuse in the big data era. With a focus on Continuous Data Protection (CDP) and Data Obliviousness, this study investigates cutting-edge techniques for improving data security and privacy. By guaranteeing real-time data backups, CDP lowers the possibility of data loss due to cyberattacks or system malfunctions. Data Obliviousness processes data securely without disclosing sensitive information by utilising methods including homomorphic encryption, secure multiparty computation (SMC), and differential privacy. When these strategies are combined in big data environments, a strong security framework is produced, regulatory compliance with the likes of CCPA and GDPR is guaranteed, and cyber threat resistance is improved.

References

Goel, P., Patel, R., Garg, D., & Ganatra, A. (2021, May). A review on big data: privacy and security challenges. In 2021 3rd International Conference on Signal Processing and Communication (ICPSC) (pp. 705-709). IEEE

Zhang, Y., Zhang, C., & Xu, Y. (2021). Effect of data privacy and security investment on the value of big data firms. Decision Support Systems, 146, 113543

Bentotahewa, V., Hewage, C., & Williams, J. (2021). Solutions to Big Data privacy and security challenges associated with COVID-19 surveillance systems. Frontiers in big data, 4, 645204

Biswas, S., Khare, N., Agrawal, P., & Jain, P. (2021). Machine learning concepts for correlated Big Data privacy. Journal of Big Data, 8(1), 157

Parihar, M. (2021). Big Data security and privacy. International Journal of Engineering Research & Technology, 10(07), 323-327

Cunha, M., Mendes, R., & Vilela, J. P. (2021). A survey of privacy-preserving mechanisms for heterogeneous data types. Computer science review, 41, 100403

Meurisch, C., & Mühlhäuser, M. (2021). Data protection in AI services: A survey. ACM Computing Surveys (CSUR), 54(2), 1-38

Salas, J., & Domingo-Ferrer, J. (2018). Some basics on privacy techniques, anonymization and their big data challenges. Mathematics in Computer Science, 12, 263-274

Katsomallos, M., Tzompanaki, K., & Kotzinos, D. (2019). Privacy, space and time: A survey on privacy preserving continuous data publishing. Journal of Spatial Information Science, 2019(19), 57-103

Kiran, A., & Devara, V. (2020). Optimal privacy preserving technique over big data analytics using oppositional fruit fly algorithm. Recent Advances in Computer Science and Communications (Formerly: Recent Patents on Computer Science), 13(2), 283-295

Vatsalan, D., Sehili, Z., Christen, P., & Rahm, E. (2017). Privacy-preserving record linkage for big data: Current approaches and research challenges. Handbook of big data technologies, 851-895

Kaaniche, N., & Laurent, M. (2017). Data security and privacy preservation in cloud storage environments based on cryptographic mechanisms. Computer Communications, 111, 120-141

Yallamelli. A. K. G., (2021). Improving Cloud Computing Data Security with the RSA Algorithm. International Journal of Information Technology and Computer Engineering, ISSN 2347–3657, Volume 9, Issue 2, 2021

BR Gudivaka., (2021). Designing AI-Assisted Music Teaching with Big Data Analysis. Journal of Current Science & Humanities, 9 (4), 2021, 1-14

Published

2022-03-27

How to Cite

Swapna Narla. (2022). BIG DATA PRIVACY AND SECURITY USING CONTINUOUS DATA PROTECTION DATA OBLIVIOUSNESS METHODOLOGIES. Journal of Science & Technology , 7(2), 423–436. https://doi.org/10.46243/jst.2022.v7.i02.pp423-436