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Recently, the ubiquity of mobile devices leads to an increasing demand of public network services, e.g., WiFi hot spots. As a part of this trend, modern transportation systems are equipped with public WiFi devices to provide Internet access for passengers as people spend a large amount of time on public transportation in their daily life. However, one of the key issues in public WiFi spots is the privacy concern due to its open access nature. Existing works either studied location privacy risk in human traces or privacy leakage in private networks such as cellular networks based on the data from cellular carriers. To the best of our knowledge, none of these work has been focused on bus WiFi privacy based on large-scale real-world data. In this paper, to explore the privacy risk in bus WiFi systems, we focus on two key questions how likely bus WiFi users can be uniquely re-identified if partial usage information is leaked and how we can protect users from the leaked information. To understand the above questions, we conduct a case study in a large-scale bus WiFi system, which contains 20 million connection records and 78 million location records from 770 thousand bus WiFi users during a two-month period. Technically, we design two models for our uniqueness analyses and protection, i.e., a PB-FIND model to identify the probability a user can be uniquely re-identified from leaked information; a PB-HIDE model to protect users from potentially leaked information. Specifically, we systematically measure the user uniqueness on users' finger traces (i.e., connection URL and domain), foot traces (i.e., locations), and hybrid traces (i.e., both finger and foot traces). Our measurement results reveal (i) 97.8% users can be uniquely re-identified by 4 random domain records of their finger traces and 96.2% users can be uniquely re-identified by 5 random locations on buses; (ii) 98.1% users can be uniquely re-identified by only 2 random records if both their connection records and locations are leaked to attackers. Moreover, the evaluation results show our PB-HIDE algorithm protects more than 95% users from the potentially leaked information by inserting only 1.5% synthetic records in the original dataset to preserve their data utility.
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This content will become publicly available on July 1, 2026
"Free WiFi is not ultimately free": Privacy Perceptions of Users in the US regarding City-wide WiFi Services
City-wide free WiFi is one of the most common initiatives of smart city infrastructures. While city-wide free WiFi services are not subject to privacy-focused regulations and appeal to a broader demographic, how users perceive privacy in such services is unknown. This study explores perspectives of users in the United States regarding the privacy practices of such services as well as their expectations. We conducted surveys with 199 participants of US, consisting of those who had used such services (i.e., experienced users, n=99) and those who had not (i.e., potential users, n=100), assessing their satisfaction with the services, perceptions regarding data privacy practices of city-wide free WiFi services, and general expectations of privacy. We identify 14 key findings by analyzing the responses from participants. We found that participants are aware of the data collection and data sharing by the WiFi services and are uncomfortable with both but are still inclined to use the services as the need for WiFi outweighs privacy, as well as because of the significant trust they place in the services due to their non-profit and government-run nature. Our analysis provides actionable takeaways for researchers and practitioners, arguing for long-term privacy gains through a regulatory approach that treats city-wide WiFi as a utility, given the trust consumers place in it, and the overall tendency of consumers to trade-off privacy for WiFi access in this context.
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- Award ID(s):
- 2132281
- PAR ID:
- 10655379
- Publisher / Repository:
- Proceedings on Privacy Enhancing Technologies
- Date Published:
- Journal Name:
- Proceedings on Privacy Enhancing Technologies
- Volume:
- 2025
- Issue:
- 3
- ISSN:
- 2299-0984
- Page Range / eLocation ID:
- 527 to 544
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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