In this paper, we study the correlation between passwords across different datasets which quantitatively explains the success of existing training-based password cracking techniques. We also study the correlation between a user's password and his/her social profile. This enabled us to develop the first social profile-aware password strength meter, namely SociaLShield. Our quantification techniques and SocialShield have meaningful implications to system administrators, users, and researchers, e.g., helping them quantitatively understand the threats posed by a password leakage incident, defending against emerging profile-based password attacks, and facilitating the research of countermeasures against existing and newly developed training-based password attacks. We validate our proposed quantification techniques and SocialShield through extensive experiments by leveraging real-world leaked passwords. Experimental results demonstrate that our quantification techniques are accurate in measuring correlation among different leaked datasets and that although SocialShield is light-weight, it is effective in defending against profile-based password attacks.