The tech industry (e.g., Apple, Google) has been grappling very publicly with the conflicting issues of collecting user data on the one hand and trying to protect the privacy of the source of that data on the other hand.  Think of a spectrum of less data/more privacy on one end moving toward more data/less privacy on the other end.  Apple tends to operate at the former end of that spectrum and Google at the latter end.  This “conflict” between data collection and source privacy is the same issue that is playing out in the precision agriculture industry.  Seed companies, agricultural consultants, and service providers all want, and need, a lot of ag data.  The farming community is, correctly, very concerned about privacy of that data which is, itself, often very proprietary.  That’s the same conundrum that the tech industry is facing.

Yesterday, at a public presentation of the new operating systems for Macintosh computers and iOS devices, Apple noted that it will be using a statistical tool known as “differential privacy” to collect user data (in order to enhance its services, improve search results, etc.) in a way that apparently has been computationally proven to protect user/customer privacy.  The use of a similar tool in the precision ag industry should provide comfort to farmers that their data and the identity of the source of the data is protected.  If such demonstrated techniques are used in a robust fashion, that will also substantially ease the contract issues between a farmer and a precision ag data user.

A description of the differential privacy approach was published last evening on Wired magazine’s web site.  The article includes a link to a more rigorous description of the system.  There may be some additional complexity in the ag industry because of the importance of fairly specific location of the data being analyzed, but it is certainly possible that a collection of important characteristics of a specific location (soil characteristics, some sort of classification of past performance in terms of yield, etc.) might be able to be identified that would provide the precision ag industry enough high quality data without having to know, or collect, identifiable information such as GPS coordinates.

For additional insights on legal issues related to technology and data privacy in precision agriculture, contact a member of Husch Blackwell’s Precision Agriculture team.