Preserving Privacy in Mobile Social Networks by Personalization of Fine Grained Spam Filtering Scheme
Author(s):
Savita Baban Ghatte, Pravin B. Ghewari, Prathmesh S. Powar
Keywords:
Fine-grained, mobile communication network (MSN), personalized, privacy protection, spam filter
Abstract
A mobile communication network (MSN) emerges as a promising social network paradigm that enables mobile users to share information closely and facilitate their cyber-physical-social interaction. As ads, rumors, and spam spread across MSN, it is necessary to filter out spam before they reach the recipients to make MSN work properly. In this regard, we propose a well-designed filtering system (PIF) with confidentiality on MSNs. Exactly; begin to develop a community-based filter distribution scheme, in which filters create filters for their social networks (i.e., filter holders). These filters retain filters and decide to block spam or transfer the desired packets through keyword filters with rough and refined characters. At the same time, advanced cryptographic filter schemes protect the privacy of the creator (i.e., keyword) embedded in the filters so that they can be disclosed directly to other users. In addition, we develop the Merkle Hash tree to store filters as leaf nodes where filters creators can check that distributed filters need to be updated by finding the root node value. It is shown that PIF can protect users' passwords that have been filtered from being disclosed to others and detected forged filters.
Article Details
Unique Paper ID: 156009

Publication Volume & Issue: Volume 9, Issue 2

Page(s): 561 - 564
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