Achieving Highest Privacy Preservation Using Efficient Machine Learning Technique
Keywords:
Privacy, Privacy Preservation, Data Modification, Machine Learning, Gradient descent, Multi Party PrivacyAbstract
Data privacy has become a paramount concern in big data, prompting the development of encryption algorithms and security strategies to safeguard sensitive information. Centralized machine learning approaches often involve transferring data to a central point to train models, which poses a risk of data exposure because unauthorized persons can disclose our private data publicly. To address this issue, multi-party privacy protection combined with machine learning offers a solution, with machine learning emerging as a way to ensure privacy in multi-party settings. This paper presents EPFML (Efficient Privacy Framework Using Machine Learning) that employs data modification. The algorithm enables joint model training while maintaining multi-party security. We use gradient descent with encrypted data transmission, preventing data exposure during the process. To counter member inference attacks, we employ data modification on the data, ensuring data privacy. Our approach demonstrates applicability across various domains, offering a privacy-protected multi-party machine learning framework. Experimental results indicate the efficiency and accuracy of our method, paving the way for enhanced data security and privacy in multi-party learning environments.
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