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@article{173183,
        author = {Nikhil Gupta},
        title = {Privacy-Preserving Generative AI Techniques in Smart Home Appliances: A Comprehensive Review},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {9},
        pages = {2352-2357},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=173183},
        abstract = {The increasing adoption of smart home appliances has brought significant improvements to daily life, but it has also raised concerns about data privacy and security. Generative AI, a powerful tool with the ability to create new content and analyze existing data , offers promising solutions for addressing these concerns. This paper provides a comprehensive review of privacy-preserving generative AI techniques in smart home appliances, examining their potential to enhance data protection. The review explores various methods, including differential privacy, federated learning, and homomorphic encryption, discussing their strengths, limitations, and applications in the context of smart homes. It also delves into key research findings and discusses the challenges and opportunities associated with implementing these techniques. By examining the current state of research and highlighting future directions, this paper aims to contribute to the development of privacy-preserving AI solutions that can foster trust and ensure the responsible use of generative AI in smart homes.},
        keywords = {Data Protection, Differential Privacy, Federated Learning, GANs, Generative AI, Homomorphic Encryption, Privacy, Smart Homes, VAEs.},
        month = {February},
        }