Whether they're looking for nearby restaurants, wondering what to wear, or finding the fastest route, most people allow their smart phones to send their GPS locations to Yelp, AccuWeather, or Google Maps without a second thought. But these data can be shared with advertisers and other third parties that profile users' movement patterns, often without their knowledge.

Even anonymizing people's location data doesn't necessarily protect their privacy. When New York City released anonymized data on more than 173 million taxi trips in response to a Freedom of Information Act request in March, researchers quickly combined the data with known reference points—addresses, for example—to pinpoint celebrities' cab trips and identify who frequented local strip clubs.

Computer scientists are devising countermeasures. CacheCloak, a system developed by researchers at Duke University in Durham, North Carolina, throws off tracking efforts by hiding users' actual location data. When you want to find, say, nearby restaurants, CacheCloak doesn't send Yelp or Google your exact GPS coordinates, but an entire path that it predicts you will take. That path is made to intersect with predicted paths from other users, so that the service sees requests from a series of interweaving paths where a driver can go either way at each crossing, and cannot track any single user. But consumers can still receive relatively accurate results.

A slightly different camouflage strategy is to send dummy locations along with a user's real location. Researchers at Microsoft, for instance, have built an algorithm that can generate realistic car trips in Seattle based on real GPS data on 16,000 drives taken by about 250 volunteer drivers in the area. The dummy trips have plausible start and end points—no stopping in the middle of a highway—adhere to speed limits, and deliberately follow slightly nonoptimal routes, so that a filter can't easily pick out the false trips from the real ones. A mobile phone would draw on the library of routes to send both the user's actual location and points from many dummy trips to a cloud-based location service like Google Maps. The app responds—say, to a request for traffic warnings—for all locations, but users can use the answers they need and disregard the rest.

The downside of the strategy is that such dummy searches can result in embarrassment, says computer scientist Michael Herrmann of the University of Leuven in Belgium. For example, many people might not want their trip to the library masked as a visit to an HIV testing site.

In a third strategy, algorithms can simply send imprecise location data to services, cloaking a user's whereabouts in 1-kilometer squares rather than revealing precise GPS coordinates. But that has the obvious drawback of decreasing the quality of an online service, Herrmann says. For a weather app, your exact location may not matter, but if you're on foot and need to find a nearby ATM, precision is crucial.

In the end, human movements are often so predictable that they are hard to conceal. Location-hiding techniques are most valuable when you want to hide one-off trips, Herrmann says. But when it comes to protecting the location of your home and workplace, you might as well give up on privacy.