data privacy

Precision Agriculture Series: Data Challenge #4- Data Privacy

This is the fifth post in our “Precision Agriculture series.” In this part, we’ll address the fourth challenge of data labeling: data privacy. Be sure to stay tuned for our sixth post addressing the fifth and last challenge in our precision agriculture series: smart tooling. 

For companies in the AgriTech field, like many others – having to comply with data privacy requirements is not a small task. Data confidentiality and privacy regulations like GDPR, DPA, and CCPA restrict the data that businesses can draw on. It is important to keep in mind that while companies are gathering even more data and tapping into its value, data confidentiality laws are growing at the same speed.

But you might ask yourself, “it’s smart farming – what’s the problem with non-personal data?” Well, farms collect data on animals, which in turn directly corresponds to the owner of that livestock. For crops, the data relates directly to the farmers’ personal details. Then comes the issue of remote-controlled drones or tractors that have the ability to monitor their users and track their performance, as well as identify them. This is where data privacy issues occur, and AgriTech suppliers need to address these concerns in a constructive manner. 

Complying with data privacy requirements for farming data

All consumer data has to be anonymized, which slows down the data labeling process and increases the amount of work. When it comes to labeling unstructured data, this includes anonymizing or blurring faces, license plates, and any other identifying data that might accidentally appear in the high-resolution, large-scale images generated by drones.

According to GDPR, Article 5, enterprises are obligated to process data lawfully, fairly and in a transparent manner in relation to the subject matter. The purpose of this limitation is that data should only be collected for specific, explicit, and legitimate purposes. This data cannot be processed further in a manner that is incompatible with its original intention for which it was collected for.   

This process ensures that the data is secure, and prevents data labeling workers from accessing it from an insecure device, downloading and transferring it to an unknown storage location, or working on data in a public location where it could be viewed by someone without security clearance.

We’ve seen that in practice, this usually means that data has to be managed and stored on-premise and accessed only from approved devices. We realize that this makes it challenging for organizations dealing with data to outsource tasks to third-party data labeling providers, while still complying with regulations. It limits where employees can work and adds yet another layer of complexity to workforce management

Bottom Line

It’s essential that you and your data labeling service know which laws you need to follow and how to ensure compliance, whether your data is stored on-premise or in the cloud.

In our next post, we’ll dive into the fifth challenge of data labeling: Smart tooling. In the meantime, if you’d like to learn more about Dataloop’s precision agriculture solution, click here.

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