InsightaaS: The Net-Savvy Executive is a blog by Nathan Gilliatt, an analyst who focuses on market intelligence and social media/analytics. In the post featured today, Gilliatt presents a seven-stage framework that can be used to assess the factors that would limit the insights that can be collected via electronic surveillance. He divides the list into two groups. “Hard constraints, which operate independently of judgement and decisions by surveillance operators” include data existence (if the data doesn’t exist, it can’t be used/abused) and “technical,” a category that covers engryption, strong passwords and other “barriers to surveillance.” The “soft constraints” category covers factors that “depend on human judgement, decisionmaking and enforcement.” These include “legal” (which varies by jurisdiction), “market” (negative reactions from customers/citizens/voters), “policy” (which is often a reaction to market pressures), “ethical,” and “personal.”
By itself, a framework of this sort doesn’t provide an answer to companies, managers and/or individuals asking what their response should be to the seemingly-endless potential for intrusive data collection; as Gilliatt says, “this framework isn’t meant to answer the big questions; it’s about structuring an exploration of the tradeoffs we make between the utility and the costs of surveillance.” Viewed through that lens, the model is useful. People will need to make data use/protection decisions on their own (Gilliatt also notes that “easy answers [to these issues] don’t exist, or they’re wrong”] – but tools that help to frame questions and responses are and will continue to be helpful, as questions of data collection and privacy are and continue to be important to anyone connected to the Internet.
As ubiquitous surveillance is increasingly the norm in our society, what are the options for limiting its scope? What are the levers that we might pull? We have more choices that you might think, but their effectiveness depends on which surveillance we might hope to limit.
One night last summer, I woke up with an idea that wouldn’t leave me alone. I tried the old trick of writing it down so I could forget it, but more details kept coming, and after a couple of hours I had a whiteboard covered in notes for a book on surveillance in the private sector (this was pre-Snowden, and I wasn’t interested in trying to research government intelligence activities). Maybe I’ll even write it eventually.
The release of No Place to Hide, Glenn Greenwald’s book on the Snowden story, provides the latest occasion to think about the challenges and complexity of privacy and freedom in a data-saturated world. I think the ongoing revelations have made clear that surveillance is about much more than closed-circuit cameras, stakeouts and hidden bugs. Data mining is a form of passive surveillance, working with data that has been created for other purposes.
Going wide to frame the question
As I was thinking about the many ways that we are watched, I wondered what mechanisms might be available to limit them. I wanted to be thorough, so I started with a framework to capture all of the possibilities. Here’s what I came up with…