Measuring risks inherent to our digital economies using Amazon purchase histories from US consumers

• URL: http://arxiv.org/abs/2502.18774v1
• Date: Wed, 26 Feb 2025 03:06:04 GMT
• Title: Measuring risks inherent to our digital economies using Amazon purchase histories from US consumers
• Authors: Alex Berke, Kent Larson, Sandy Pentland, Dana Calacci,
• Abstract summary: We show that purchases for pickles and trampolines risk revealing clues about customers' personal attributes - in this case, their race.<n>This work provides the first open analysis measuring these risks, using purchase histories crowdsourced from (N=4248) US Amazon.com customers.<n>We demonstrate how easily consumers' personal attributes, such as health and lifestyle information, gender, age, and race, can be inferred from purchases.<n>To better understand the risks that highly resourced firms like Amazon, data brokers, and advertisers present to consumers, we measure how our models' predictive power scales with more data.
• Score: 6.090518383948741
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: What do pickles and trampolines have in common? In this paper we show that while purchases for these products may seem innocuous, they risk revealing clues about customers' personal attributes - in this case, their race. As online retail and digital purchases become increasingly common, consumer data has become increasingly valuable, raising the risks of privacy violations and online discrimination. This work provides the first open analysis measuring these risks, using purchase histories crowdsourced from (N=4248) US Amazon.com customers and survey data on their personal attributes. With this limited sample and simple models, we demonstrate how easily consumers' personal attributes, such as health and lifestyle information, gender, age, and race, can be inferred from purchases. For example, our models achieve AUC values over 0.9 for predicting gender and over 0.8 for predicting diabetes status. To better understand the risks that highly resourced firms like Amazon, data brokers, and advertisers present to consumers, we measure how our models' predictive power scales with more data. Finally, we measure and highlight how different product categories contribute to inference risk in order to make our findings more interpretable and actionable for future researchers and privacy advocates.

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