Privacy-Preserving Feature Selection with Fully Homomorphic Encryption

Abstract

For the feature selection problem, we propose an efficient privacy-preserving algorithm. Let $D$, $F$, and $C$ be data, feature, and class sets, respectively, where the feature value $x(F_i)$ and the class label $x(C)$ are given for each $x\in D$ and $F_i\in F$. For a triple $(D,F,C)$, the feature selection problem is to find a consistent and minimal subset $F^{\prime }\subseteq F$, where `consistent' means that, for any $x,y\in D$, $x(C)=y(C)$ if $x(F_i)=y(F_i)$ for $F_i\in F^{\prime }$, and `minimal' means that any proper subset of $F^{\prime }$ is no longer consistent. On distributed datasets, we consider feature selection as a privacy-preserving problem: Assume that semi-honest parties $\text{A}$ and $\text{B}$ have their own personal $D_{\text{A}}$ and $D_{\text{B}}$. The goal is to solve the feature selection problem for $D_{\text{A}}\cup D_{\text{B}}$ without revealing their privacy. In this paper, we propose a secure and efficient algorithm based on fully homomorphic encryption, and we implement our algorithm to show its effectiveness for various practical data. The proposed algorithm is the first one that can directly simulate the CWC (Combination of Weakest Components) algorithm on ciphertext, which is one of the best performers for the feature selection problem on the plaintext.

14 pages

References in corpus (1)

• Privacy-Preserving Feature Selection with Secure Multiparty Computation

Cited by in corpus (1)

• Algorithmic Data Minimization for Machine Learning over Internet-of-Things Data Streams