Kesarwani, M and Kaul, A and Singh, G and Deshpande, PM and Haritsa, JR (2018) Collusion-Resistant Processing of SQL Range Predicates. In: Data Science and Engineering, 3 (4). pp. 323-340.
|
PDF
Dat_Sci_Eng_3-4_323_2018.pdf - Published Version Download (3MB) | Preview |
Abstract
Prior solutions for securely handling SQL range predicates in outsourced Cloud-resident databases have primarily focused on passive attacks in the Honest-but-Curious adversarial model, where the server is only permitted to observe the encrypted query processing. We consider here a significantly more powerful adversary, wherein the server can launch an active attack by clandestinely issuing specific range queries via collusion with a few compromised clients. The security requirement in this environment is that data values from a plaintext domain of size N should not be leaked to within an interval of size H. Unfortunately, all prior encryption schemes for range predicate evaluation are easily breached with only O(log 2 �) range queries, where �= N/ H. To address this lacuna, we present SPLIT, a new encryption scheme where the adversary requires exponentially more�O(�) �range queries to breach the interval constraint and can therefore be easily detected by standard auditing mechanisms. The novel aspect of SPLIT is that each value appearing in a range-sensitive column is first segmented into two parts. These segmented parts are then independently encrypted using a layered composition of a secure block cipher with the order-preserving encryption and prefix-preserving encryption schemes, and the resulting ciphertexts are stored in separate tables. At query processing time, range predicates are rewritten into an equivalent set of table-specific sub-range predicates, and the disjoint union of their results forms the query answer. A detailed evaluation of SPLIT on benchmark database queries indicates that its execution times are well within a factor of two of the corresponding plaintext times, testifying its efficiency in resisting active adversaries. © 2018, The Author(s).
| Item Type: | Journal Article |
|---|---|
| Publication: | Data Science and Engineering |
| Publisher: | Springer |
| Additional Information: | Copy right for this article belongs to Springer |
| Department/Centre: | Division of Electrical Sciences > Computer Science & Automation Division of Interdisciplinary Sciences > Computational and Data Sciences |
| Date Deposited: | 16 Apr 2019 06:47 |
| Last Modified: | 16 Apr 2019 06:47 |
| URI: | http://eprints.iisc.ac.in/id/eprint/62116 |
Actions (login required)
 |
View Item |
