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

IdleLeak: Exploiting Idle State Side Effects for Information Leakage

Fabian Rauscher (Graz University of Technology), Andreas Kogler (Graz University of Technology), Jonas Juffinger (Graz University of Technology), Daniel Gruss (Graz University of Technology)

Read More

Experimental Analyses of the Physical Surveillance Risks in Client-Side...

Ashish Hooda (University of Wisconsin-Madison), Andrey Labunets (UC San Diego), Tadayoshi Kohno (University of Washington), Earlence Fernandes (UC San Diego)

Read More

Low-Quality Training Data Only? A Robust Framework for Detecting...

Yuqi Qing (Tsinghua University), Qilei Yin (Zhongguancun Laboratory), Xinhao Deng (Tsinghua University), Yihao Chen (Tsinghua University), Zhuotao Liu (Tsinghua University), Kun Sun (George Mason University), Ke Xu (Tsinghua University), Jia Zhang (Tsinghua University), Qi Li (Tsinghua University)

Read More

GraphGuard: Detecting and Counteracting Training Data Misuse in Graph...

Bang Wu (CSIRO's Data61/Monash University), He Zhang (Monash University), Xiangwen Yang (Monash University), Shuo Wang (CSIRO's Data61/Shanghai Jiao Tong University), Minhui Xue (CSIRO's Data61), Shirui Pan (Griffith University), Xingliang Yuan (Monash University)

Read More