Open AccessFine Grained Access Control on Information Retrieval with Collaborative Fusion and Active Feedback
Author(s) -
Dinesha L*,
S Kumaraswamy
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.j9790.0981119
Subject(s) - computer science , information retrieval , human–computer information retrieval , access control , data retrieval , relevance feedback , relevance (law) , cognitive models of information retrieval , information access , search engine , world wide web , image retrieval , artificial intelligence , computer network , political science , law , image (mathematics)
Enterprise information retrieval is quite challenging than web based information retrieval due to retrieval from heterogeneous distributed data sources and need for higher accuracy in retrieval [17]. In our earlier work, collaborative fusion guided by active feedback is proposed for information retrieval in enterprise environment. Applying three dimensions of user similarity, user-document search history and document similarity, collaborative information fusion based retrieval was proposed in that work However the work did not consider enforcing fine grained access control on the retrieval which is very important requirement in enterprise environment. In this work, fine grained access controlled information retrieval on enterprise environment, with zero leakage assurance thorough direct or inference is proposed. Fine grained access control is ensured with help of CP-ABE. Concept masking is done with CP-ABE and unmasked when user satisfies the query access right criteria.