Hexuan Yu (Virginia Polytechnic Institute and State University), Changlai Du (Virginia Polytechnic Institute and State University), Yang Xiao (University of Kentucky), Angelos Keromytis (Georgia Institute of Technology), Chonggang Wang (InterDigital), Robert Gazda (InterDigital), Y. Thomas Hou (Virginia Polytechnic Institute and State University), Wenjing Lou (Virginia Polytechnic Institute and State University)

Mobile tracking has long been a privacy problem, where the geographic data and timestamps gathered by mobile network operators (MNOs) are used to track the locations and movements of mobile subscribers. Additionally, selling the geolocation information of subscribers has become a lucrative business. Many mobile carriers have violated user privacy agreements by selling users' location history to third parties without user consent, exacerbating privacy issues related to mobile tracking and profiling. This paper presents AAKA, an anonymous authentication and key agreement scheme designed to protect against mobile tracking by honest-but-curious MNOs. AAKA leverages anonymous credentials and introduces a novel mobile authentication protocol that allows legitimate subscribers to access the network anonymously, without revealing their unique (real) IDs. It ensures the integrity of user credentials, preventing forgery, and ensures that connections made by the same user at different times cannot be linked. While the MNO alone cannot identify or profile a user, AAKA enables identification of a user under legal intervention, such as when the MNOs collaborate with an authorized law enforcement agency. Our design is compatible with the latest cellular architecture and SIM standardized by 3GPP, meeting 3GPP's fundamental security requirements for User Equipment (UE) authentication and key agreement processes. A comprehensive security analysis demonstrates the scheme's effectiveness. The evaluation shows that the scheme is practical, with a credential presentation generation taking ~52 ms on a constrained host device equipped with a standard cellular SIM.

View More Papers

Strengthening Privacy in Robust Federated Learning through Secure Aggregation

Tianyue Chu, Devriş İşler (IMDEA Networks Institute & Universidad Carlos III de Madrid), Nikolaos Laoutaris (IMDEA Networks Institute)

Read More

MPCDiff: Testing and Repairing MPC-Hardened Deep Learning Models

Qi Pang (Carnegie Mellon University), Yuanyuan Yuan (HKUST), Shuai Wang (HKUST)

Read More

COSPAS Search and Rescue Satellite Uplink: A MAC-Based Security...

Syed Khandker (New York University Abu Dhabi), Krzysztof Jurczok (Amateur Radio Operator), Christina Pöpper (New York University Abu Dhabi)

Read More

Separation is Good: A Faster Order-Fairness Byzantine Consensus

Ke Mu (Southern University of Science and Technology, China), Bo Yin (Changsha University of Science and Technology, China), Alia Asheralieva (Loughborough University, UK), Xuetao Wei (Southern University of Science and Technology, China & Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, SUSTech, China)

Read More