Identifying unfamiliar callers’ professions from privacy-preserving mobile phone data

Abstract

Identifying an unfamiliar caller’s profession is important to protect citizens’ personal safety and property. Due to limited data protection of many popular online services in some countries such as taxi hailing or takeouts ordering, many users encounter an increasing number of phone calls from strangers. This may aggravate the situation that criminals pretend to be delivery staff or taxi drivers, bringing threats to the society. Additionally, many people nowadays suffer from excessive digital marketing and fraud phone calls because of personal information leakage. However, previous works on malicious call detection only focused on binary classification, and do not work for identification of multiple professions. We observed that web service requests issued from users’ mobile phones which may show their Apps preferences, spatial and temporal patterns, and other profession related information. This offers us a hint to identify unfamiliar callers. In fact, some previous works already leveraged raw data from mobile phones (which includes sensitive information) for personality studies. However, accessing users’ mobile phone raw data may violate the more and more strict private data protection policies or regulations (e.g. GDPR 71). Using appropriate statistical methods to eliminate private information and preserve personal characteristics, provides a way to identify mobile phone callers without privacy concern. In this paper, we exploit privacy-preserving mobile data to develop a model which can automatically identify the callers who are divided into four categories of users: normal users (other professions), taxi drivers, delivery and takeouts staffs, telemarketers and fraudsters. The validation results over an anonymized dataset of 1,282 users with a period of 3 months in Shanghai City prove that the proposed model could achieve an accuracy of 75+%.