SYNTHETIC DATA GENERATION: REVOLUTIONIZING AI DEVELOPMENT AND PRIVACY
DOI:
https://doi.org/10.34218/IJCET_16_01_250Keywords:
Artificial Intelligence, Data Privacy, Regulatory Compliance, Synthetic Data Generation, Training Data OptimizationAbstract
The use of artificial intelligence has presented the world with the generation of synthetic data, transforming the world's approach in handling critical data challenges in relation to privacy, availability, and reduction of bias. This article explores the manner in which synthetic data technologies revolutionize all aspects of health, financial services, autonomous vehicles, and retailing.Synthetic data implementations are allowing organizations to comply with regulations like GDPR and HIPAA, thereby accelerating AI development cycles and enhancing model performance across various applications. This article explores how synthetic data is changing the landscape of AI development and data privacy by looking into technical foundations like various generation techniques, such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Agent-Based Models, and analyzing their effectiveness in preserving data utility while being compliant with privacy regulations, industry applications of synthetic data, and future trends.
References
Bogdan Nedić, et al., "Gartner's Top Strategic Technology Trends," in Proceedings of the 13th International Quality Conference, Faculty of Engineering, University of Kragujevac, 2019. [Online]. Available: https://www.cqm.rs/2019/papers_iqc/41.pdf
Alex X. Wang, et al., "Enhancing Data Governance through Data-Centric AI: Case Study in New Zealand Government Sector," 16th International Conference on Computer and Automation Engineering (ICCAE), 2024. [Online]. Available: https://ieeexplore.ieee.org/document/10569739
Yaganteeswarudu Akkem, et al., "A comprehensive review of synthetic data generation in smart farming by using variational autoencoder and generative adversarial network," Engineering Applications of Artificial Intelligence, Volume 131, May 2024, 107881. [Online]. Available: https://www.sciencedirect.com/science/article/abs/pii/S0952197624000393
William Gu, "Privacy preserving synthetic data generation," B.S. thesis, Dept. Math. and Comp. Sci., Freie Universität Berlin, Berlin, Germany, 2021. [Online]. Available: https://www.mi.fu-berlin.de/inf/groups/ag-idm/theseses/2021_Gu_BSc.pdf
Aldren Gonzales, et al., "Synthetic data in health care: A narrative review," PLOS Digit Health. 2023 Jan 6;2(1). [Online]. Available: https://pmc.ncbi.nlm.nih.gov/articles/PMC9931305/
Samuel Assefa, et al., "Generating synthetic data in finance: opportunities, challenges and pitfalls," 33rd Conference on Neural Information Processing Systems (NeurIPS 2019). [Online]. Available: https://www.jpmorgan.com/content/dam/jpm/cib/complex/content/technology/ai-research-publications/pdf-8.pdf
Keerthana V, et al., "GANs for Synthetic Data Generation: Advancements and Challenges using Machine Learning," International Journal of Advance Research, Ideas and Innovations in Technology, 2024. [Online]. Available: https://www.ijariit.com/manuscripts/v10i1/V10I1-1234.pdf
Hugo Wai Leung Mak, et al., "Application of Variational AutoEncoder (VAE) Model and Image Processing Approaches in Game Design," Sensors, vol. 23, no. 7, p. 3457, 2023. [Online]. Available: https://www.mdpi.com/1424-8220/23/7/3457
Krishna Kanagarla, "The Role of Synthetic Data in Ensuring Data Privacy and Enabling Secure Analytics," European Journal of Engineering and Technology Research 10(11):75-79, 2024. Available: https://www.researchgate.net/publication/386046387_The_Role_of_Synthetic_Data_in_Ensuring_Data_Privacy_and_Enabling_Secure_Analytics
Ofer Mendelevitch, et al., "Fidelity and Privacy of Synthetic Medical Data," arXiv preprint arXiv:2101.08658, 2021. [Online]. Available: https://arxiv.org/abs/2101.08658
Abdul Majeed, et al., "When AI Meets Information Privacy: The Adversarial Role of AI in Data Sharing Scenario," IEEE Internet of Things Journal, vol. 10, no. 12, pp. 18234-18247, 2023. [Online]. Available: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10190078
[12] Mohamed Ashik Shahul Hameed, et al., "Bias Mitigation via Synthetic Data Generation: A Review," Electronics 2024, 13(19), 3909. [Online]. Available: https://www.mdpi.com/2079-9292/13/19/3909
Mandeep Goyal, et al., "A Systematic Review of Synthetic Data Generation Techniques Using Generative AI," Electronics, vol. 13, no. 17, p. 3509, 2024. [Online]. Available: https://www.mdpi.com/2079-9292/13/17/3509
Daniel Carvajal-Patiño, et al., "Synthetic data generation with deep generative models to enhance predictive tasks in trading strategies," Research in International Business and Finance, Volume 62, December 2022, 101747. [Online]. Available: https://www.sciencedirect.com/science/article/abs/pii/S0275531922001313
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