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Unmasking Concealed 5G Privacy Identity with Machine Learning and GPU in 12 mins
  • Vui Huang Tea
Vui Huang Tea
Independent Research

Corresponding Author:[email protected]

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Abstract

The 3rd Generation Partnership Project (3GPP) standard for 5G telecommunications specifies privacy protection schemes to cryptographically encrypt and conceal permanent identifiers of subscribers to prevent them from being exposed and tracked by over-the-air eavesdroppers. However, conventional privacy-preserving protocols and architectures alone are insufficient to protect subscriber privacy as they are vulnerable to new types of attacks due to the utilization of the emerging technologies such artificial intelligence (AI). A conventional brute force attack to unmask concealed 5G identity using a CPU would require ~877 million years. This paper presents an apparatus using machine learning (ML) and a graphics processing unit (GPU) that is able to unmask a concealed 5G identity in ~12 minutes with an untrained neural-network, or ~0.015 milliseconds with a pre-trained neural-network. The 5G concealed identities are effectively identified without requiring decryption, hence severely diminishing the level of privacy-preservation. Finally, several ML defence countermeasures are proposed to re-establish privacy protection in 5G identity.