Arjun Arunasalam (Purdue University), Andrew Chu (University of Chicago), Muslum Ozgur Ozmen (Purdue University), Habiba Farrukh (University of California, Irvine), Z. Berkay Celik (Purdue University)

The impact of e-commerce on today’s society is a global phenomenon. Given the increased demand for online purchases of items, e-commerce platforms often defer item sales to third-party sellers. A number of these sellers are dropshippers, sellers acting as middlemen who fulfill their customers’ orders through third-party suppliers. While this allows customers to access more products on e-commerce sites, we uncover that abusive dropshippers, who exploit the standard permitted dropshipping model, exist, deceiving customers, and damaging other e-commerce sellers. In this paper, we present the first comprehensive study on the characterization of abusive dropshippers and uncover harmful strategies they use to list items and evade account suspension on e-commerce marketplaces. We crawled the web to discover online forums, instructional material, and software used by the abusive dropshipping community. We inductively code forum threads and instructional material and read software documentation, installing when possible, to create an end-to-end lifecycle of this abuse. We also identify exploitative strategies abusive dropshippers use to ensure persistence on platforms. We then interviewed six individuals experienced in e-commerce (legal consultants and sellers) and developed an understanding of how abusive dropshipping harms customers and sellers. Through this, we present five characteristics that warrant future investigation into automated detection of abusive dropshippers on e-commerce platforms. Our efforts present a comprehensive view of how abusive dropshippers operate and how sellers and consumers interact with them, providing a framework to motivate future investigations into countering these harmful operations.

View More Papers

A Two-Layer Blockchain Sharding Protocol Leveraging Safety and Liveness...

Yibin Xu (University of Copenhagen), Jingyi Zheng (University of Copenhagen), Boris Düdder (University of Copenhagen), Tijs Slaats (University of Copenhagen), Yongluan Zhou (University of Copenhagen)

Read More

On the Vulnerability of Traffic Light Recognition Systems to...

Sri Hrushikesh Varma Bhupathiraju (University of Florida), Takami Sato (University of California, Irvine), Michael Clifford (Toyota Info Labs), Takeshi Sugawara (The University of Electro-Communications), Qi Alfred Chen (University of California, Irvine), Sara Rampazzi (University of Florida)

Read More

Modeling and Detecting Internet Censorship Events

Elisa Tsai (University of Michigan), Ram Sundara Raman (University of Michigan), Atul Prakash (University of Michigan), Roya Ensafi (University of Michigan)

Read More

SLMIA-SR: Speaker-Level Membership Inference Attacks against Speaker Recognition Systems

Guangke Chen (ShanghaiTech University), Yedi Zhang (National University of Singapore), Fu Song (Institute of Software, Chinese Academy of Sciences; University of Chinese Academy of Sciences)

Read More