Computer Science ›› 2018, Vol. 45 ›› Issue (8): 7-12.doi: 10.11896/j.issn.1002-137X.2018.08.002

• ChinaMM 2017 • Previous Articles     Next Articles

Study on Wi-Fi Fingerprint Anonymization for Users in Wireless Networks

HAN Xiu-ping1, WANG Zhi1, PEI Dan2   

  1. Department of Computer Science and Technology,Graduate School at Shenzhen,Tsinghua University,Shenzhen,Guangdong 518055,China1
    Department of Computer Science and Technology,Tsinghua University,Beijing 100084,China2
  • Received:2017-10-24 Online:2018-08-29 Published:2018-08-29

Abstract: Billions of Wi-Fi assess points (APs) have been deployed to provide wireless connection to people with different kinds of mobile devices.Toaccelerate the speed of Wi-Fi connection,mobile devices will send probe requests to discover nearby Wi-Fi APs,and maintain previously connected network lists (PNLs) of APs.Previous studies show that the Wi-Fi fingerprints that consist of probed SSIDs individually will leak private information of users.This paper investigated the privacy caused by the Wi-Fi fingerprints in the wild,and provided a data-driven solution to protect privacy.First,measurement studies were carried out based on 27 million users associating with 4 million Wi-Fi APs in 4 cities,and it was revealed that Wi-Fi fingerprints can be used to identify users in a wide range of Wi-Fi scenarios.Based on semantic mining and analysis of SSIDs in Wi-Fi fingerprints,this paper further inferred demographic information of identified users (e.g.,people’s jobs),telling “who they are”.Second,this paper proposed a collaborative filtering (CF) based heuristic protection method,which can “blur” an user’s PNL by adding faked SSIDs,such that nearby users’ PNLs and Wi-Fi fingerprints are similar to each other.Finally,the effectiveness of the design was verified by using real-world Wi-Fi connection traces.The experiments show that the refined PNLs protect users’ privacy while still provide fast Wi-Fi reconnection.

Key words: Wireless network, Privacy leakage, Protection, Probe request frame, User behavior

CLC Number: 

  • TP393
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