The exponential growth in smartphone adoption is contributing to the availability of vast amounts of human behavioral data. This data enables the development of increasingly accurate data-driven user models that facilitate the delivery of personalized services that are often free in exchange for the use of its customers’ data. Although such usage conventions have raised many privacy concerns, the increasing value of personal data is motivating diverse entities to aggressively collect and exploit the data. In this article, we unfold profiling scenarios around mobile HTTP(S) traffic, focusing on those that have limited but meaningful segments of the data. The capability of the scenarios to profile personal information is examined with real user data, collected in the wild from 61 mobile phone users for a minimum of 30 days. Our study attempts to model heterogeneous user traits and interests, including personality, boredom proneness, demographics, and shopping interests. Based on our modeling results, we discuss various implications to personalization, privacy, and personal data rights.

中文翻译：

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当更简单的数据并不意味着更少的信息时

智能手机采用率呈指数增长，有助于获得大量人类行为数据。这些数据支持开发越来越准确的数据驱动用户模型，从而促进个性化服务的交付，这些服务通常是免费的，以换取客户数据的使用。尽管此类使用约定引发了许多隐私问题，但个人数据的价值不断增加正促使各种实体积极收集和利用数据。在本文中，我们围绕移动 HTTP(S) 流量展开分析场景，重点关注那些数据片段有限但有意义的场景。使用真实用户数据检查场景分析个人信息的能力，这些数据从 61 名手机用户野外收集至少 30 天。我们的研究试图对不同的用户特征和兴趣进行建模，包括个性、无聊倾向、人口统计和购物兴趣。根据我们的建模结果，我们讨论了对个性化、隐私和个人数据权利的各种影响。