11 min listen
Shadow Profiles on Social Networks
FromData Skeptic
ratings:
Length:
39 minutes
Released:
Feb 13, 2015
Format:
Podcast episode
Description
Emre Sarigol joins me this week to discuss his paper Online Privacy as a Collective Phenomenon. This paper studies data collected from social networks and how the sharing behaviors of individuals can unintentionally reveal private information about other people, including those that have not even joined the social network! For the specific test discussed, the researchers were able to accurately predict the sexual orientation of individuals, even when this information was withheld during the training of their algorithm.
The research produces a surprisingly accurate predictor of this private piece of information, and was constructed only with publically available data from myspace.com found on archive.org. As Emre points out, this is a small shadow of the potential information available to modern social networks. For example, users that install the Facebook app on their mobile phones are (perhaps unknowningly) sharing all their phone contacts. Should a social network like Facebook choose to do so, this information could be aggregated to assemble "shadow profiles" containing rich data on users who may not even have an account.
The research produces a surprisingly accurate predictor of this private piece of information, and was constructed only with publically available data from myspace.com found on archive.org. As Emre points out, this is a small shadow of the potential information available to modern social networks. For example, users that install the Facebook app on their mobile phones are (perhaps unknowningly) sharing all their phone contacts. Should a social network like Facebook choose to do so, this information could be aggregated to assemble "shadow profiles" containing rich data on users who may not even have an account.
Released:
Feb 13, 2015
Format:
Podcast episode
Titles in the series (100)
[MINI] Bayesian Updating: In this minisode, we discuss Bayesian Updating - the process by which one can calculate the most likely hypothesis might be true given one's older / prior belief and all new evidence. by Data Skeptic