Privacy-Preserving k-Nearest Neighbor Classification over Malicious Participants in Outsourced Cloud Environments

In recent years, many companies have chosen to outsource data and other data computation tasks to cloud service providers to reduce costs and increase efficiency. However, there are risks of security and privacy breaches when users outsource data to a cloud environment. Many researchers have proposed schemes based on cryptographic primitives to address these risks under the assumption that the cloud is a semi-honest participant and query users are honest participants. However, in a real-world environment, users’ data privacy and security may be threatened by the presence of malicious participants. Therefore, a novel scheme based on secure multi-party computation is proposed when attackers gain control over both the cloud and a query user in the paper. We prove that our solution can satisfy our goals of security and privacy protection. In addition, our experimental results based on simulated data show feasibility and reliability.