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More data types more problems: A temporal analysis of complexity, stability, and sensitivity in privacy policies

J. Lovato, P. Mueller, P. Suchdev, and P. S. Dodds

Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT '23, 1088–1100, 2023

Times cited: 0

Abstract:

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.
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BibTeX:

@Inproceedings{lovato2023a,
  author =	 {Lovato, Juniper and Mueller, Philip and Suchdev,
                  Parisa and Dodds, Peter Sheridan},
  title =	 {More data types more problems: {A} temporal analysis
                  of complexity, stability, and sensitivity in privacy
                  Policies},
  year =	 {2023},
  publisher =	 {Association for Computing Machinery},
  address =	 {New York, NY, USA},
  url =		 {https://doi.org/10.1145/3593013.3594065},
  doi =		 {10.1145/3593013.3594065},
  booktitle =	 {Proceedings of the 2023 ACM Conference on Fairness,
                  Accountability, and Transparency},
  pages =	 {1088–1100},
  numpages =	 {13},
  keywords =	 {Privacy, Data Ethics, Data Science, Networks, Data
                  Privacy, Privacy Policies, NLP},
  location =	 {Chicago, IL, USA},
  series =	 {FAccT '23},
  note =	 {Preprint also available online at
                  \href{https://arxiv.org/abs/2302.08936}{https://arxiv.org/abs/2302.08936}},
}
 
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