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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Quantifying location privacy in permissioned blockchain-based internet of things (IoT)
Recently, blockchain has received much attention from the mobility-centric Internet of Things (IoT). It is deemed the key to ensuring the built-in integrity of information and security of immutability by design in the peer-to-peer network (P2P) of mobile devices. In a permissioned blockchain, the authority of the system has control over the identities of its users. Such information can allow an ill-intentioned authority to map identities with their spatiotemporal data, which undermines the location privacy of a mobile user. In this paper, we study the location privacy preservation problem in the context of permissioned blockchain-based IoT systems under three conditions. First, the authority of the blockchain holds the public and private key distribution task in the system. Second, there exists a spatiotemporal correlation between consecutive location-based transactions. Third, users communicate with each other through short-range communication technologies such that it constitutes a proof of location (PoL) on their actual locations. We show that, in a permissioned blockchain with an authority and a presence of a PoL, existing approaches cannot be applied using a plug-and-play approach to protect location privacy. In this context, we propose BlockPriv, an obfuscation technique that quantifies, both theoretically and experimentally, the relationship between privacy and utility in order to dynamically protect the privacy of sensitive locations in the permissioned blockchain.
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- Award ID(s):
- 1851890
- NSF-PAR ID:
- 10390162
- Editor(s):
- Vincent Poor and Zhu Han
- Date Published:
- Journal Name:
- MobiQuitous '19: Proceedings of the 16th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services
- Page Range / eLocation ID:
- 116 to 125
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3360774.3360800
