**6. Conclusions**

Since their emergence about a decade ago, social networks have rapidly grown and infiltrated every aspect of our daily lives. With rapidly expanding reliance on their platforms, social networks like Facebook and Twitter are becoming a goldmine of personal information and user behavior data which makes the study of these networks of prime importance. The valuable information stored within these platforms makes them the target of malicious entities which try to compromise the privacy of the users which may further lead to unwanted disclosure of the sensitive attributes of the network.

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In this chapter, we have reviewed a novel privacy measure that quantifies the resistance of a large social network against a privacy violating attack. We reviewed some efficient algorithms to compute this measure in social graph and revisited the privacy violation properties in 8 real-world networks. The current theoretical and empirical results for ð Þ *k*, ℓ -anonymity pave the way for further investigation of this measure, as well as addressing its shortcomings and limitations.
