An Efficient Privacy-Preserving Randomization- Based Approach for Classification Upon Encrypted Data in Outsourced Semi-Honest Environment

In cloud environment context, organizations often rely on the platform for data storage and on demand access. Data is typically encrypted either by the cloud service itself or by the data owners before outsourcing it to maintain confidentiality. However, when it comes to processing encrypted data for tasks like k NN classification; existing approaches either prove to be inefficient or delegate portion of the classification task to end users or do not satisfy all the privacy requirements. Also, the datasets used in many existing approaches to check the performance seem to have very less attributes and instances; but, it is observed that as dataset size increases, the efficiency and accuracy of many privacy-preserving approaches reduce significantly. In this work, we propose a set of privacy preserving protocols that collectively perform the k NN classification with encrypted data in outsourced semi-honest-cloud environment and also address the stated challenges. This is accomplished by building an efficient randomization-based approach called PPkC that leverages homomorphic cryptosystem properties. With protocol analysis we prove that the proposed approach satisfies all privacy requirements. Finally, with extensive experimentation using real-world and scaled dataset we show that the performance of proposed PPkC protocol is computationally efficient and also independent of the number of nearest neighbours considered.