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Location Privacy: How to Keep Your Photos (and Their Data) Safe

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How to Keep Your Photos (and Data) Safe

by Amar Toor on Switched.com, January 24, 2011 at 10:45 AM

iPhones, BlackBerrys and other smartphones have made it remarkably easy for us to share photos on sites like Facebook and Flickr. But they’ve made it a lot easier for cyberstalkers to track us, as well. That’s because many digital photos contain a kind of encoded data known as Exchangeable Image File Format (EXIF). This type of data is often used by professional photographers, since it reveals detailed information on when the photo was taken, whether it was shot with a flash, and whether any digital manipulations were applied after the initial shoot. In many cases, EXIF can even determine where a photo was taken, thanks to GPS-generated geotags attached to every shot. And this information often stays with the photo even after a user uploads it to a social network or photo-sharing site. …

For full text of the article, visit How to Keep Your Photos (and Their Data) Safe.

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Inferring Social Ties from Geographic Coincidences

Inferring social ties from geographic coincidences

We investigate the extent to which social ties between people can be inferred from co-occurrence in time and space: Given that two people have been in approximately the same geographic locale at approximately the same time, on multiple occasions, how likely are they to know each other? Furthermore, how does this likelihood depend on the spatial and temporal proximity of the co-occurrences? Such issues arise in data originating in both online and offline domains as well as settings that capture interfaces between online and offline behavior. Here we develop a framework for quantifying the answers to such questions, and we apply this framework to publicly available data from a social media site, finding that even a very small number of co-occurrences can result in a high empirical likelihood of a social tie. We then present probabilistic models showing how such large probabilities can arise from a natural model of proximity and co-occurrence in the presence of social ties. In addition to providing a method for establishing some of the first quantifiable estimates of these measures, our findings have potential privacy implications, particularly for the ways in which social structures can be inferred from public online records that capture individuals’ physical locations over time.

For full text of the article, click here.

Source: PNAS, December 27, 2010, Vol. 107, Issue 52

Authors: David J. Crandalla, Lars Backstromb, Dan Cosleyc, Siddharth Surib,Daniel Huttenlocherb, and Jon Kleinbergb.

Edited by: Ronald L. Graham, University of California, San Diego, La Jolla, CA, and approved October 25, 2010 (received for review May 16, 2010).

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