A Study on Autoencoder-based Reconstruction Method for Wi-Fi Location Data with Erasures

Abstract

Anonymization is one of the major processes to protect location-based services (LBS) from privacy leakage. However, there are many discussions about de-anonymization attacks to LBS and whether anonymization processing is a sufficient countermeasure for privacy leakage. In this paper, we proposed a novel method to reconstruct the location of user considering the time series using the Markov Transition Field (MTF) and Denoising Auto Encoder (DAE). We also focused on Wi-Fi location data including many erasures errors. We conducted an evaluation of de-anonymization attack using our reconstruction method to the Wi-Fi location dataset that was consisted of 10000 devices / four weeks in the four wards of Tokyo. We confirmed that the successful attack rate (SAR) was 24% when the number of candidate devices was 100 and 6\% when that was 10000 devices.