Cloud-based Privacy Preservation using AES and Data Recovery for Decision Tree Learning

Privacy preservation is important for machine learning and data mining, but measures designed to protect private information often result in a trade-off: reduced utility of the training samples. This paper introduces a privacy preserving approach that can be applied to decision tree learning, without concomitant loss of accuracy. The Algorithm we use in our project is the AES (Advanced Encryption Standard) Algorithm so that we can encrypt the collected data and it will be stored in cloud. It describes an approach to the preservation of the privacy of collected data samples in cases where information from the sample database has been partially lost. Data loss can have severe consequences for businesses, making data backup and recovery practices critical for protecting valuable information. Cloud storage has emerged as a powerful tool in the fight against data loss, offering secure off-site storage and robust recovery solutions.