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Collecting personally identifiable information (PII) on data subjects hasbecome big business. Data brokers and data processors are part of amulti-billion-dollar industry that profits from collecting, buying, and sellingconsumer data. Yet there is little transparency in the data collection industrywhich 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 orderto investigate the data collection activities of data brokers and dataprocessors. We also develop an original lexicon of PII-related termsrepresenting PII data types curated from legislative texts. This mesoscaleanalysis looks at privacy policies overtime on the word, topic, and networklevels to understand the stability, complexity, and sensitivity of privacypolicies over time. We find that (1) privacy legislation correlates withchanges in stability and turbulence of PII data types in privacy policies; (2)the complexity of privacy policies decreases over time and becomes moreregularized; (3) sensitivity rises over time and shows spikes that arecorrelated with events when new privacy legislation is introduced.

More Data Types More Problems: A Temporal Analysis of Complexity, Stability, and Sensitivity in Privacy Policies