Adapting Privacy

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Lawyers will be lawyers.

That's a quote from a piece at Ars Technica on how and why search engine startup Neeva changed their privacy policy. Their business model relied on users paying Neeva to keep their data private. The goal being a secure platform that considered users privacy. An admirable goal, and one that I might have been welling to fund. There is a lot of data out there, and while I don't mind companies improving their service to me with data about me, I don't like them sharing it.

In this case, Neeva didn't read their terms of service very well. I don't think this was malicious, but it was a mistake. I'm sure they used a legal group that someone recommended to them, and the lawyers were trying to craft a document that was structured to protect the company and allow for flexibility. Sharing data with affiliates and advertisers would seem to go against their model, but it's something lawyers might think is fine. If you read the article, you can see how Neeva has adjusted their terms.

As the world grows more digital, and we all deal with an ever increasing number of organizations, data privacy will become more important to some, perhaps many people. The ideas of reducing data retention, limiting transfer of data, and giving users more control is an idea that may take hold, especially if a few companies start to succeed with this business model. We've seen pressure on companies throughout this year cause them to shift their previous stances, sometimes dramatically.

For those of us that work with data, this might mean easier administration in some ways. Certainly we will have to alter our systems for archival or more flexibly data retention policies. We might even need to adapt our data models to include more fields that allow us to easily decide what data to keep, remove, or obfuscate things.

In fact, I wonder if that's the future of privacy. We de-link old users by randomly setting values for PII data and keeping other information like sales values or URLs clicked as useful information for aggregation without any risk of disclosure.

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