Secure Computation over Encrypted Databases

TL;DR

This paper reveals vulnerabilities in existing encrypted database schemes that compromise data and key privacy, and proposes improved encryption methods to prevent such attacks with minimal performance impact.

Contribution

It identifies a critical attack on key confidentiality in secure k-nearest neighbors schemes and introduces enhanced encryption techniques to safeguard data privacy.

Findings

Attack can extract secret encryption keys from tailored queries

Recovered keys enable full data decryption, breaking data privacy

Proposed schemes prevent key extraction with minimal performance overhead

Abstract

Sensitive applications running on the cloud often require data to be stored in an encrypted domain. To run data mining algorithms on such data, partially homomorphic encryption schemes (allowing certain operations in the ciphertext domain) have been devised. One such line of work yields schemes for secure \textit{k-nearest neighbors} computation that is designed to provide both \textit{Data Privacy} and \textit{Query Privacy}. Enhancements in this area further ensure that the data owner approves each query issued by a query user before the cloud server processes it. In this work, we describe an attack that invalidates the \textit{key confidentiality} claim, which further invalidates the \textit{Data Privacy} claim for these schemes. We show that a query user can specially tailor a query to extract information about the secret key used to encrypt the data points. Furthermore, the recovered secret information can be used to derive all the plaintext data points breaking \textit{data privacy}. We then suggest enhanced encryption schemes that make such attacks on \textit{data privacy} impossible while incurring meager additional costs in performance.