2023/02/17 by Juniper Lovato, Lovato, Juniper, Philip Mueller +5 · 1 citation
Computer Science · Social Sciences · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Privacy, Security, and Data Protection #Privacy-Preserving Technologies in Data #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.2302.08936
openalex publication_date 2023/02/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Collecting personally identifiable information (PII) on data subjects has become big business. Data brokers and data processors are part of a multi-billion-dollar industry that profits from collecting, buying, and selling consumer data. Yet there is little transparency in the data collection industry which makes it difficult to understand what types of data are being collected, used, and sold, and thus the risk to individual data subjects. In this study, we examine a large textual dataset of privacy policies from 1997-2019 in order to investigate the data collection activities of data brokers and data processors. We also develop an original lexicon of PII-related terms representing PII data types curated from legislative texts. This mesoscale analysis looks at privacy policies overtime on the word, topic, and network levels to understand the stability, complexity, and sensitivity of privacy policies over time. We find that (1) privacy legislation correlates with changes in stability and turbulence of PII data types in privacy policies; (2) the complexity of privacy policies decreases over time and becomes more regularized; (3) sensitivity rises over time and shows spikes that are correlated with events when new privacy legislation is introduced.