  Data mining has various real-time applications in fields such as finance
telecommunications, biology, and government. Classification is a primary task
in data mining. With the rise of cloud computing, users can outsource and
access their data from anywhere, offloading data and it is processing to the
cloud. However, in public cloud environments while data is often encrypted, the
cloud service provider typically controls the encryption keys, meaning they can
potentially access the data at any time. This situation makes traditional
privacy-preserving classification systems inadequate. The recommended protocol
ensures data privacy, protects user queries, and conceals access patterns.
Given that encrypted data on the cloud cannot be directly mined, we focus on a
secure k nearest neighbor classification algorithm for encrypted, outsourced
data. This approach maintains the privacy of user queries and data access
patterns while allowing effective data mining operations to be conducted
securely in the cloud. With cloud computing, particularly in public cloud
environments, the encryption of data necessitates advanced methods like secure
k nearest neighbor algorithms to ensure privacy and functionality in data
mining. This innovation protects sensitive information and user privacy,
addressing the challenges posed by traditional systems where cloud providers
control encryption keys.
