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2007 IEEE 23rd International Conference on Data Engineering
Invalidation Clues for Database Scalability Services
Istanbul, Turkey
April 15-April 20
ISBN: 1-4244-0802-4
Amit Manjhi, Carnegie Mellon University
Phillip B. Gibbons, Intel Research Pittsburgh
Anastassia Ailamaki, Carnegie Mellon University
Charles Garrod, Carnegie Mellon University
Bruce M. Maggs, Carnegie Mellon University; Akamai Technologies
Todd C. Mowry, Carnegie Mellon University; Intel Research Pittsburgh
Christopher Olston, Yahoo! Research; Carnegie Mellon University
Anthony Tomasic, Carnegie Mellon University
Haifeng Yu, National University of Singapore
For their scalability needs, data-intensive Web applications can use a Database Scalability Service (DBSS), which caches applications' query results and answers queries on their behalf. One way for applications to address their security/privacy concerns when using a DBSS is to encrypt all data that passes through the DBSS. Doing so, however, causes the DBSS to invalidate large regions of its cache when data updates occur. To invalidate more precisely, the DBSS needs help in order to know which results to invalidate; such help inevitably reveals some properties about the data. In this paper, we present invalidation clues, a general technique that enables applications to reveal little data to the DBSS, yet limit the number of unnecessary invalidations. Compared with previous approaches, invalidation clues provide applications significantly improved tradeoffs between security/privacy and scalability. Our experiments using three Web application benchmarks, on a prototype DBSS we have built, confirm that invalidation clues are indeed a low-overhead, effective, and general technique for applications to balance their privacy and scalability needs.
Citation:
Amit Manjhi, Phillip B. Gibbons, Anastassia Ailamaki, Charles Garrod, Bruce M. Maggs, Todd C. Mowry, Christopher Olston, Anthony Tomasic, Haifeng Yu, "Invalidation Clues for Database Scalability Services," icde, pp.316-325, 2007 IEEE 23rd International Conference on Data Engineering, 2007
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