Title:
Efficient and Secure Search of Enterprise File Systems
Efficient and Secure Search of Enterprise File Systems
Author(s)
Singh, Aameek
Srivatsa, Mudhakar
Liu, Ling
Srivatsa, Mudhakar
Liu, Ling
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Abstract
With fast paced growth of enterprise data, quickly locating relevant content has become a critical IT capability. Research
has shown that nearly 85% of enterprise data lies in flat filesystems [12] that allow multiple users and user groups with
different access privileges to underlying data. Any search tool for such large scale systems needs to be efficient and
yet cognizant of the access control semantics imposed by the underlying filesystem. Current multiuser enterprise search
techniques use two disjoint search and access-control components by creating a single system-wide index and simply
filtering search results for access control. This approach is ineffective as the index and query statistics subtly leak private
information. The other available approach of using separate indices for each user is undesirable as it not only increases
disk consumption due to shared files, but also increases the overheads of updating the indices whenever a file changes.
We propose a distributed approach that couples search and access-control into a unified framework and provides
secure multiuser search. Our scheme (logically) divides data into independent access-privileges based chunks, called
access-control barrels (ACB). ACBs not only manage security but also improve overall efficiency as they can be indexed
and searched in parallel by distributing them to multiple enterprise machines. We describe the architecture of ACBs based
search framework and propose two optimization technique that ensure the scalability of our approach. We also discuss
other useful features of our approach – seamless integration with desktop search and an extenstion to provide secure search
in untrusted storage service provider environments. We validate our approach with a detailed evaluation using industry
benchmarks and real datasets. Our initial experiments show secure search with 38% improved indexing efficiency and
low overheads for ACB processing.
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Date Issued
2007
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Text
Resource Subtype
Technical Report