A measurement study on Amazon wishlist and its privacy exposure

User preference plays an important factor in E-commerce websites for advertising and marketing, and the disclosure of user preference could also raise privacy concerns. As one of the largest E-commerce platform, Amazon features a wishlist that allows users to keep track of their desired products. In this paper, we investigate Amazon wishlist, and its possible privacy exposure. To this end, we collect complete wishlists of over 30,000 users, by analyzing which we are able to make interesting observations based on user online shopping preference in multiple dimensions. Specifically, we show user preference variation from different demographical groups, including gender and geo-locations. Taking timing factors into consideration, we also observe that unlike traditional walk-in-shop type of shopping, there is no significant difference in the dynamics of Amazon wishlists between weekdays and weekend. In the investigation of user information exposure in Amazon wishlists, we parse and analyze list-descriptions, illustrating which and to what extent user personal information is exposed to the public. Finally, we demonstrate that the information in wishlists has potential to leak a user's private personal information. Based on the collected user data, we can predict user gender with over 80% accuracy by just exploiting items present in Amazon wishlists.