Amara Javed

Work place: Department of Computer Science, GC Women University, Sialkot, Pakistan

E-mail: amarajaved706@gmail.com

Website:

Research Interests: Information Security, Network Architecture, Network Security, Information-Theoretic Security

Biography

Amara Javed was born in Sialkot, Pakistan in 1997. She received the degree of Bachelors in information technology from GC Women University, Sialkot, Pakistan in 2015 and currently doing MS in computer science. Her research interest including recommender system and network security.

Author Articles
H2E: A Privacy Provisioning Framework for Collaborative Filtering Recommender System

By Muhammad Usman Ashraf Mubeen Naeem Amara Javed Iqra Ilyas

DOI: https://doi.org/10.5815/ijmecs.2019.09.01, Pub. Date: 8 Sep. 2019

A Recommender System (RS) is the most significant technologies that handle the information overload problem of Retrieval Information by suggesting users with correct and related items. Today, abundant recommender systems have been developed for different fields and we put an effort on collaborative filtering (CF) recommender system. There are several problems in the recommender system such as Cold Start, Synonymy, Shilling Attacks, Privacy, Limited Content Analysis and Overspecialization, Grey Sheep, Sparsity, Scalability and Latency Problem. The current research explored the privacy in CF recommender system and defined the perspective privacy attributes (user's identity, password, address, and postcode/location) which are required to be addressed. Using the base models as Homomorphic and Hash Encryption scheme, we have proposed a hybrid model Homomorphic Hash Encryption (H2E) model that addressed the privacy issues according to defined objectives in the current study. Furthermore, in order to evaluate the privacy level, H2E was implementing in medicine recommender system and compared the consequences with existing state-of-the-art privacy protection mechanisms. It was observed that H2E outperform to other models with respect to determined privacy objectives. Leading to user's privacy, H2E can be considered a promising model for CF recommender systems.

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