The real key is going to be figuring out how to prevent the kind of correlation that I describe. I am not sure it can be done. We may well have to accept the loss of privacy as a side consequence of the benefits.
But then, privacy is a cultural concept anyway. People who deal with shared living spaces, such as a six-person family in a two-bedroom house, have a different expecation of privacy than those who have one-person spaces.
It's also a matter of trust. Private simply means information that you want to be able to control access to, and you might be willing to share that information with those you trust not to use it to harm you or more widely if you trust that the information cannot be used in any way to harm you.
@RichQ: Oddly, perhaps, I don't mind anyone knowing those movement pattern, as long as they cannot be connected directly to me.
As always, you hit the nail on the head. I totally agree with this -- I think the whole concept of "Big Data" is tremendously exciting when applied to the way in which people do "stuff," but I prefer it to be used as a generalization of lots of people and not to focus on individuals.
It seems to me that the key to privacy is to disconnect location information from the identity. If I place a 911 call, of course, I will want to be sure that the location information is linked to the call details (such as stating the nature of the medical emergency). But otherwise, I would prefer that no one is able to determine where I am and infer what I am doing based on my movement patterns.
Oddly, perhaps, I don't mind anyone knowing those movement pattern, as long as they cannot be connected directly to me. I can see great benefit to folks like highway planners, for instance, knowing when and how the commuters get from their homes to their offices. That, to me, does not violate privacy. But being able to extract the endpoint addresses of a given movement, and correlate that with a name, is a violation of privacy.
This presents a problem because there are other information sources that can be correlated with the data to "fill in the gaps" of such an association. Simply knowing where movement starts and stops, if correlated with housing and employment records, would be enough to determine with fair accuracy who the movement record is about. So, it's not enough to simply separate the identity from the data.
What the solution to this issue might be, I haven't got a clue.
@Edward: How does this added locating functionality affect our chnaging sense of privacy?
I think the key point is when you say "our changing sense of privacy" -- things certainly are changing.
If you were to go back say 100 years ago -- you could "pick up sticks" and move to a new town and create a new identity.... on the other hand, when you did move to a new place people tended to be inquisitive -- and a lot of ladies stayed at home and looked out of windows -- pretty much everyone in a small town knew what everyone else was doing.
These days people tend to leave you alone and they aren't so nosy -- but your likes/dislikes etc. can be tracked via online means.
This is a really complicated area -- there are lots of advantages to having your information out there -- but lots of disadvantages also.
In the case of a 911 call, it would make sense to me that even if you've set your cell phone to be in "stealth mode" -- not reporting your location or whatever, if you make a 911 call, that overrides the privicy settings...
Good point, Max. More apps give us better locating functionality. Now to inject a (perhaps) not directly relevant point: How does this added locating functionality affect our chnaging sense of privacy?
There's also the fact that things like smartphones are becoming increasingly stuffed with sensors (magnetometers, gyros, accelerometers....) coupled with the computational processing (in software and hardware) to make them context aware so they knwo if you are sitting, leaning against a wall, going up/down in/on an elevator/escalator ... so they can add this info into the location-determining mix...
A Book For All Reasons Bernard Cole1 Comment Robert Oshana's recent book "Software Engineering for Embedded Systems (Newnes/Elsevier)," written and edited with Mark Kraeling, is a 'book for all reasons.' At almost 1,200 pages, it ...