This paper proposes a novel method to perform privacy-preserving face identification based
on the notion of group testing, and applies it to a solution using the Cheon-Kim-Kim-Song …

Manhattan distance is mainly used to calculate the total absolute wheelbase of two points in
the standard coordinate system. The secure computation of Manhattan distance is a new …

We propose a novel privacy-preserving, two-party computation of various distance metrics
(eg, Hamming distance, Scalar Product) followed by a comparison with a fixed threshold …

Existing secure two-party Euclidean distance computation schemes are mostly performed
based on a semi-honest model, which faces bottlenecks in computation efficiency and …

The advent of the big data era has brought unprecedented data demands. The integration of
computing resources with network resources in the computing force network enables the …

Computing the distance between two non-normalized vectors x and y, represented by Δ (x,
y) and comparing it to a predefined public threshold τ is an essential functionality used in …

This paper proposes a novel collaborative decryption protocol for the Brakerski-Fan-
Vercauteren (BFV) homomorphic encryption scheme in a multiparty distributed setting, and …

Computing distance metrics of sensitive data and comparing to a predefined public
threshold in a privacy-preserving way is an indispensable building block for a wide range of …

While biometric data, such as fingerprints, are increasingly used for identification and
authentication, their inability to be revoked once compromised raises privacy concerns. To …

We propose an efficient quantum private comparison protocol firstly based on one direction
quantum walks. With the help of one direction quantum walk, we develop a novel method …