WPF urges National Science Foundation to study Statistical Parity

WPF’s Public comments on National Privacy Research Strategy

The World Privacy Forum submitted comments today to the National Science Foundation in response to its request for information about a national privacy research strategy. WPF urged a research focus on statistical parity and its implementation. Statistical parity is a term WPF’s Pam Dixon coined at the FTC’s Big Data, Tool For Inclusion or Exclusion? workshop in September 2014. Here is Dixon’s definition of the term:

“Statistical parity means ensuring that all parts of the consumer data analytics process are fair: data collection, which data factors chosen and used for analytics, accuracy of the factors and how well the algorithm works for its intended purpose, and then how the final results are vetted and used, and for how long. Statistical parity means finding ways to ensure privacy and fairness in the analytics process from beginning to end, and to ensure that decisions about consumers are accurate and used fairly and in a non-discriminatory way.” Comments to NSF, Oct. 17, 2014

In their comments, the WPF also asked for research on granular consent mechanisms and on consumer segmentation, scoring, and modeling.

WPF encouraged the study of data provenance and also study of layered protection models, a concept WPF has been researching. From the comments:

On layered protection models:

“We have hypothesized that a layered, overlapping protections approach is going to work best going forward. FIPs plus statistical parity, for example, working together as a model. Perhaps a responsible use framework could be added on top of this, in addition to, but not replacing the other frameworks. The days and era of having a single-silver bullet solution to privacy are long gone, and it is much more helpful to find effective ways of layering protections and building in individual consumer choice – meaningful choice – wherever possible. We would like to see a request for scenarios on this point. We emphasize, however, that any model that leaves out FIPs will be incomplete in approach.”

On meta tagging and data provenance:

“In some circumstances, it will be appropriate to tag data so that the source of the data is transferred whenever the data is transferred. In other words, we suggest that metadata be employed to show the provenance of the data. This is technically possible and already happens to some extent in Electronic Health Records, and. It has great potential for privacy. We observe in passing that better provenance of data has great value to researchers as well.”

To see more, read the full comments.

WPF’s Public comments on National Privacy Research Strategy (PDF)