Beware of What You Share: Inferring User Locations in Venmo

被引：5
作者：
Yao, Xin [1 ,2 ]
Chen, Yimin [1 ]
Zhang, Rui [3 ]
Zhang, Yanchao [1 ]
Lin, Yaping [2 ]
机构：
[1] Arizona State Univ, Ira A Fulton Sch Engn, Sch Elect Comp & Energy Engn, Tempe, AZ 85287 USA
[2] Hunan Univ, Coll Comp Sci & Elect Engn, Changsha 410012, Hunan, Peoples R China
[3] Univ Delaware, Dept Comp & Informat Sci, Newark, DE 19716 USA
来源：
IEEE INTERNET OF THINGS JOURNAL | 2018年 / 5卷 / 06期
基金：
中国国家自然科学基金; 美国国家科学基金会;
关键词：
Location inference; mobile payment; privacy; security; BELIEF-PROPAGATION; PRIVACY;
D O I：
10.1109/JIOT.2018.2844218
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Mobile payment apps are seeing explosive usage worldwide. This paper focuses on Venmo, a very popular mobile person-to-person payment service owned by Paypal. Venmo allows money transfers between users with a mandatory transaction note. More than half of transaction records in Venmo are public information. In this paper, we propose a multilayer location inference (MLLI) technique to infer user locations from public transaction records in Venmo. MLLI explores two observations. First, many Venmo transaction notes contain implicit location cues. Second, the types and temporal patterns of user transactions have strong ties to their location closeness. With a large dataset of 2.12M users and 20.23M Venmo transaction records, we show that MLLI can identify the top-1, top-3, and top-5 possible locations for a Venmo user with accuracy up to 50%, 80%, and 90%, respectively. Our results highlight the danger of sharing transaction notes on Venmo or similar mobile payment apps.
引用
收藏
页码：5109 / 5118
页数：10
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