  The popularity of the online media-driven social network relation is proven
in today's digital era. The many challenges that these emergence has created
include a huge growing network of social relations, and the large amount of
data which is continuously been generated via the different platform of social
networking sites, viz. Facebook, Twitter, LinkedIn, Instagram, etc. These data
are Personally Identifiable Information (PII) of the users which are also
publicly available for some platform, and others allow with some restricted
permission to download it for research purposes. The users' accessible data
help in providing with better recommendation services to users, however, the
PII can be used to embezzle the users and cause severe detriment to them.
Hence, it is crucial to maintain the users' privacy while providing their PII
accessible for various services. Therefore, it is a burning issue to come up
with an approach that can help the users in getting better recommendation
services without their privacy being harmed. In this paper, a framework is
suggested for the same. Further, how data through Twitter API can be crawled
and used has been extensively discussed. In addition to this, various security
and legal perspectives regarding PII while crawling the data is highlighted. We
believe the presented approach in this paper can serve as a benchmark for
future research in the field of data privacy.
