Address Authentication Method for Sustainable Social Qualification

• Main Authors: Hosung Park, Seungsoo Nam, Daeseon Choi
• Format: Article
• Language: English
• Published: MDPI AG 2020-02-01
• Series: Sustainability
• Subjects: authentication; machine learning; clustering algorithm; location of interest
• Online Access: https://www.mdpi.com/2071-1050/12/5/1700

This paper proposes an address authentication method based on a user’s location history. Address authentication refers to actual residence verification, which can be used in various fields such as personnel qualification, online identification, and public inquiry. In other words, accurate address authentication methods can reduce social cost for actual residence verification. For address authentication, existing studies discover the user’s regular locations, called location of interest (LOI), from the location history by using clustering algorithms. They authenticate an address if the address is contained in one of the LOIs. However, unnecessary LOIs, which are unrelated to the address may lead to false authentications of illegitimate addresses, that is, other users’ addresses or feigned addresses. The proposed method tries to reduce the authentication error rate by eliminating unnecessary LOIs with the distinguishing properties of the addresses. In other words, only few LOIs that satisfy the properties (long duration, high density, and consistency) are kept and utilized for address authentication. Experimental results show that the proposed method decreases the authentication error rate compared with previous approaches using time-based clustering and density-based clustering.