Encrypted Yet Leaking: Analyzing Side-Channel Vulnerabilities in Location Privacy of LBS

With the widespread use of smartphones and the rapid growth in mobile users worldwide, Location-Based Services (LBS) have become indispensable in daily life. The utilization of such services inevitably results in the generation of a considerable quantity of geolocation data. Most of these data have been encrypted to safeguard user privacy. However, the risk of side-channel information leakage still exists. In order to reveal the vulnerabilities in geolocation privacy, we propose a novel attack method, ETLA, leveraging encrypted LBS traffic analysis techniques. Specifically, we design a feature extraction algorithm, TPFC, to accurately restore the application-layer LBS transmission patterns and construct highly recognizable location combined features. Finally, we conduct extensive experiments based on real traffic datasets, and the results show that the average attack accuracy of ETLA reaches 97.68%. Additionally, we validate the application agnosticism, temporal stability and anti-interference capability of the model, further emphasizing the threat posed by the attack in real-world application scenarios.