Elijah Bouma-Sims (Carnegie Mellon University), Lily Klucinec (Carnegie Mellon University), Mandy Lanyon (Carnegie Mellon University), Julie Downs (Carnegie Mellon University), Lorrie Faith Cranor (Carnegie Mellon University)

Fraudsters often use the promise of free goods as a lure for victims who are convinced to complete online tasks but ultimately receive nothing. Despite much work characterizing these "giveaway scams," no human subjects research has investigated how users interact with them or what factors impact victimization. We conducted a scenario-based experiment with a sample of American teenagers (n = 85) and adult crowd workers (n = 205) in order to investigate how users reason about and interact with giveaway scams advertised in YouTube videos and to determine whether teens are more susceptible than adults. We found that most participants recognized the fraudulent nature of the videos, with only 9.2% believing the scam videos offered legitimate deals. Teenagers did not fall victim to the scams more frequently than adults but reported more experience searching for terms that could lead to victimization. This study is among the first to compare the interactions of adult and teenage users with internet fraud and sheds light on an understudied area of social engineering.

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DShield: Defending against Backdoor Attacks on Graph Neural Networks...

Hao Yu (National University of Defense Technology), Chuan Ma (Chongqing University), Xinhang Wan (National University of Defense Technology), Jun Wang (National University of Defense Technology), Tao Xiang (Chongqing University), Meng Shen (Beijing Institute of Technology, Beijing, China), Xinwang Liu (National University of Defense Technology)

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Trust or Bust: A Survey of Threats in Decentralized...

Hetvi Shastri (University of Massachusetts Amherst), Akanksha Atrey (Nokia Bell Labs), Andre Beck (Nokia Bell Labs), Nirupama Ravi (Nokia Bell Labs)

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Repurposing Neural Networks for Efficient Cryptographic Computation

Xin Jin (The Ohio State University), Shiqing Ma (University of Massachusetts Amherst), Zhiqiang Lin (The Ohio State University)

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Inspecting Compiler Optimizations on Mixed Boolean Arithmetic Obfuscation

Rachael Little, Dongpeng Xu (University of New Hampshire)

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