k-anonymization is a vital mechanism for protecting privacy in publishing of data. Though a large amount of work has been done, everybody has taken only single static unleash. As data is getting accumulated, data needs to be anonymized differently for different recipient or different purpose. In this context, an individual's anonymtity will get unintentionally compromised if data receiver examines all the releases published or different receivers conspire themselves even when all releases get k-anonymized properly. Major challenges need to be faced while preventing such correspondence attacks. In this paper, we describe various attacks in anonymizing incrementally updated records. © 2018 American Scientific Publishers. All rights reserved.