802.11 user fingerprinting

Pang, Jong-Shi; Greenstein, Ben; Gummadi, Ramakrishna; Seshan, Srinivasan; Wetherall, David

doi:10.1145/1287853.1287866

Cited by 170 publications

(148 citation statements)

References 18 publications

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“…The purpose of this mechanism is, that it becomes more difficult to clearly recognize a phone by probe request captures and, thus, the privacy of iPhone users is slightly increased. However, the randomization of MAC addresses alone does not truly preserve the user's privacy, due to implicit identifiers, or specific characteristics of Wi-Fi traffic [14]. Furthermore, the actual implementation of the randomization in iOS fails in practice due to several conditions, which are not common in real-life, e.g.…”

Section: Wi-fimentioning

confidence: 99%

Analyzing Pedestrian Flows Based on Wi-Fi and Bluetooth Captures

Schauer¹,

Werner²

2015

ICST Transactions on Ubiquitous Environments

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The rapid deployment of smartphones has led to a wide adoption of wireless communication systems such as Wi-Fi and Bluetooth. Both techniques leak information to the surroundings during operation. This information has been used in literature for estimating pedestrian flows, but the correlation to ground truth has not yet been evaluated. Thus, a reliable deployment in real world scenarios is rather difficult. To fill in this gap, we use ground truth provided by the security check process at a major airport and evaluate the quality of crowd information gathered from Wi-Fi and Bluetooth captures. We analyze estimated pedestrian flows and present three approaches improving the accuracy compared to a naive count of captured MAC addresses. Such counts only showed an impractical Pearson correlation of 0.53 for Bluetooth and 0.61 for Wi-Fi. The presented approaches yield a better correlation and allow for a practical estimation of pedestrian flows.

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Section: Wi-fimentioning

confidence: 99%

Analyzing Pedestrian Flows Based on Wi-Fi and Bluetooth Captures

Schauer¹,

Werner²

2015

ICST Transactions on Ubiquitous Environments

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“…Broadcasting SSIDs in plaintext results in a number of issues, as identified in [7,11,10]. Multiple solutions have been proposed to achieve privacy preserving service discovery, which we review in the following paragraphs.…”

Section: Countermeasures: How To Avoid Device Linkagementioning

confidence: 99%

“…The open nature of Wi-Fi connectivity has raised a number of privacy concerns in both the media and research literature [11,14,7], including the well publicized Google Street View collection of traffic from home Wi-Fi APs.…”

Section: Introductionmentioning

confidence: 99%

Linking wireless devices using information contained in Wi-Fi probe requests

Cunche

Kâafar

Boreli

2014

Pervasive and Mobile Computing

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Active service discovery in Wi-Fi involves wireless stations broadcasting their Wi-Fi fingerprint, i.e. the SSIDs of their preferred wireless networks. The content of those Wi-Fi fingerprints can reveal different types of information about the owner. We focus on the relation between the fingerprints and the links between the owners. Our hypothesis is that social links between devices owners can be identified by exploiting the information contained in the fingerprint. More specifically we propose to consider the similarity between fingerprints as a metric, with the underlying idea: similar fingerprints are likely to be linked. We first study the performances of several similarity metrics on a controlled dataset and then apply the designed classifier to a dataset collected in the wild. Finally we discuss potential countermeasures and propose a new one based on geolocation. This study is based on a dataset collected in Sydney, Australia, composed of fingerprints belonging to more than 8000 devices.

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“…Privacy in wireless networks: Wireless network privacy is a serious concern due to ease of eavesdropping. [27] identifies several explicit identifiers in 802.11 MAC header that can be used to identify and track users. SlyFi [13] is an 802.11-like link layer protocol that obfuscates all transmitted bits to remove explicit identifiers and increase privacy.…”

Section: B Private and Verifiable Proximity Computationmentioning

confidence: 99%

Secure friend discovery in mobile social networks

Dong

Dave

Qiu

et al. 2011

2011 Proceedings IEEE INFOCOM

135

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Abstract-Mobile social networks extend social networks in the cyberspace into the real world by allowing mobile users to discover and interact with existing and potential friends who happen to be in their physical vicinity. Despite their promise to enable many exciting applications, serious security and privacy concerns have hindered wide adoption of these networks. To address these concerns, in this paper we develop novel techniques and protocols to compute social proximity between two users to discover potential friends, which is an essential task for mobile social networks. We make three major contributions. First, we identify a range of potential attacks against friend discovery by analyzing real traces. Second, we develop a novel solution for secure proximity estimation, which allows users to identify potential friends by computing social proximity in a privacy-preserving manner. A distinctive feature of our solution is that it provides both privacy and verifiability, which are frequently at odds in secure multiparty computation. Third, we demonstrate the feasibility and effectiveness of our approaches using real implementation on smartphones and show it is efficient in terms of both computation time and power consumption.

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802.11 user fingerprinting

Cited by 170 publications

References 18 publications

Analyzing Pedestrian Flows Based on Wi-Fi and Bluetooth Captures

Analyzing Pedestrian Flows Based on Wi-Fi and Bluetooth Captures

Linking wireless devices using information contained in Wi-Fi probe requests

Secure friend discovery in mobile social networks

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