An Efficient Encrypted Floating-Point Representation Using HEAAN and TFHE

Abstract

As a method of privacy-preserving data analysis (PPDA), a fully homomorphic encryption (FHE) has been in the spotlight recently. Unfortunately, because many data analysis methods assume that the type of data is of real type, the FHE-based PPDA methods could not support the enough level of accuracy due to the nature of FHE that fixed-point real-number representation is supported easily. In this paper, we propose a new method to represent encrypted floating-point real numbers on top of FHE. e proposed method is designed to have analogous range and accuracy to 32-bit floating-point number in IEEE 754 representation. We propose a method to perform arithmetic operations and size comparison operations. e proposed method is designed using two different FHEs, HEAAN and TFHE. As a result, HEAAN is proven to be very efficient for arithmetic operations and TFHE is efficient in size comparison. is study is expected to contribute to practical use of FHE-based PPDA.

Data Security and Privacy Protection for Cloud Storage: A Survey

“…Although this scheme implements a lot of primary operations on the representation of encrypted floating-point real values, it does not support the size comparison operation for given floating-point values. In order to solve this problem, Moon and Lee [63] introduced TFHE [22] algorithm on the basis of the [20], and obtained higher performance comparison operation.…”

A Privacy-Preserving Fully Homomorphic Encryption and Parallel Computation Based Biometric Data Matching

“…We used the Euclidean distance to match the two biometric data. Consider the problem of computing the distance of two vectors x (1) and x (2) ∈ R n using the Euclidean distance. The standard Euclidean distance metric is defined as:…”

“…Our parallel execution solution is to divide the whole distance computation into T smaller computations, where T is the number of CPU cores available on a single computer. Accordingly, the vectors x (1) and x (2) can be partitioned into T sub-vectors. As a result, a possible approach for parallelizing the Euclidean distance calculation is to define the calculation task as the problem of calculating the distance between two vectors.…”

“…As a result, a possible approach for parallelizing the Euclidean distance calculation is to define the calculation task as the problem of calculating the distance between two vectors. To carry out all the necessary calculations of each subtask, must contain sub-partition of vector x (1) and x (2) . Figure 1 shows the distributed and parallel computation of the distance calculation using several CPU cores.…”

“…Biometrics matching systems are used for the identification of persons by using fingerprints, iris, and palm, etc. The biometrics matching algorithm uses a plain version of the sensitive biometrics data to achieve high-performance matching for accurate identification [1,2]. Today, many organizations, especially border control police, use such identification system to verify travelers [3].…”