Big data Privacy security is becoming the hot spot in the various social industries, because attackers can build an integrate portrait to threaten privacy of users by identifying accounts in different sites. Simulation assessment of the attacker re-identification ability is the precondition of users' privacy protection. Therefore, this paper proposes a high similarity algorithm in same day with same behaviors. The core idea of the algorithm is as follows: if a couple account issues similar or identical content on the same day, which also appears many times in different websites, then these two accounts may belong to a person with a high possibility. In addition, this paper builds a new weighting model for the users' attributes to improve the accuracy of user re-identification. After the experiment on more than ten thousand users of the two major domestic social networking site, this algorithm proves to be effective. Experimental results show that even if attacker don't consider users' social relations, the users' tweets, attributes, still provide enough information to make the attacker correlate their different accounts, which will lead to leak of more privacy.