John Kleinberg: „Challenges in Mining Social Network Data: Processes, Privacy and Paradoxes”

ePresenceTV – Challenges in Mining Social Network Data: Processes, Privacy and Paradoxes

slide#12: LiveJournal is much smaller than Facebook, but it’s a blogging site – it’s completely open. It wants to be crawled, it wants to be indexed. It becomes a model system.

slide #13: Would I join a service when I have 3 friends using it, which are connected or which are independent? Would I believe info from 3 independent persons or connected?

slide #20: Music Lab: 8 parallel instances of a music download site with feedback (rank, recommendations) -> in each instance songs at the top (download, rate) varied.

slide #24: To hack an anonymized network (just nodes & edges) attacker can add some nodes (create some accounts) and edges (send messages) before the network is anonymized and afterwards geo-locate his nodes… and others… aim of the attack: privacy breach

slide #29: added (by attacker) nodes create random graph (with edge probability 1/2) H which is unique in small worlds (easy to locate)

slide #30: after locating H -> locate target nodes the attacker linked to earlier

slide #31: if unplanned, you + 6 random friends can carry out an attack (without need of adding spam nodes before anonymization) -> you can comprise ~10 users


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