Privacy-Preserving Techniques in Cloud Computing: The Role of Homomorphic Encryption

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@article{1706387,
      author = {Ibraheem Adebayo Yoosuf},
      title = {Privacy-Preserving Techniques in Cloud Computing: The Role of Homomorphic Encryption},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {8},
      number = {4},
      pages = {211-223},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1706387.pdf},
      abstract = {Given the potential risks associated with cloud computing, industries handling sensitive information like healthcare, government, and finance, must proactively implement robust data privacy measures to protect their customers and maintain compliance. This article explores homomorphic encryption (HE) as a transformative solution for protecting this sensitive information.  It presents homomorphic encryption (HE) as a transformative solution for privacy-preserving cloud environments, enabling secure data outsourcing without revealing plaintext data. We discuss HE's ability to process encrypted data in cloud infrastructures and analyze its computational overhead and scalability limitations, which have hindered its widespread adoption. The paper further evaluates key applications, such as privacy-preserving machine learning, financial fraud detection, and genomic data analysis, where HE is highly advantageous. Compared with other privacy-preserving techniques like secure multi-party computation (SMPC) and trusted execution environments (TEEs), we emphasize HE?s unique advantage in balancing data privacy and operational efficiency. Future developments in the field, including advancements in HE schemes and integration with other privacy-preserving technologies like differential privacy and secure multi-party computation, are also considered, offering insights into its potential to become a standard in secure cloud computing.},
      keywords = {Homomorphic Encryption, Cloud Computing, Privacy-Preserving Techniques, Secure Data Outsourcing, Computational Overhead, Machine Learning, Genomic Analysis, Financial Fraud Detection.},
      month = {October},
  }