q-Anon: Rethinking Anonymity for Social Networks

Provided by: University of Colombo School of Computing
Topic: Security
Format: PDF
This paper proposes that social network data should be assumed public but treated private. Assuming this rather confusing requirement means that anonymity models such as k-anonymity cannot be applied to the most common form of private data release on the internet, social network APIs. An alternative anonymity model, q-Anon, is presented, which measures the probability of an attacker logically deducing previously unknown information from a social network API while assuming the data being protected may already be public information. Finally, the feasibility of such an approach is evaluated suggesting that a social network site such as Facebook could practically implement an anonymous API using q-Anon, providing its users with an anonymous option to the current application model.

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