Beware of What You Share: Inferring User Locations in Venmo

Xin Yao, Yimin Chen, Rui Zhang, Yanchao Zhang, Yaping Lin

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


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.

Original languageEnglish (US)
Article number8372910
Pages (from-to)5109-5118
Number of pages10
JournalIEEE Internet of Things Journal
Issue number6
StatePublished - Dec 2018


  • Location inference
  • Mobile payment
  • Privacy
  • Security

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications


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