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You searched for subject:(Outsourced Databases). Showing records 1 – 2 of 2 total matches.

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Boston University

1. Papadopoulos, Dimitrios. Function-specific schemes for verifiable computation.

Degree: PhD, Computer Science, 2016, Boston University

An integral component of modern computing is the ability to outsource data and computation to powerful remote servers, for instance, in the context of cloud computing or remote file storage. While participants can benefit from this interaction, a fundamental security issue that arises is that of integrity of computation: How can the end-user be certain that the result of a computation over the outsourced data has not been tampered with (not even by a compromised or adversarial server)? Cryptographic schemes for verifiable computation address this problem by accompanying each result with a proof that can be used to check the correctness of the performed computation. Recent advances in the field have led to the first implementations of schemes that can verify arbitrary computations. However, in practice the overhead of these general-purpose constructions remains prohibitive for most applications, with proof computation times (at the server) in the order of minutes or even hours for real-world problem instances. A different approach for designing such schemes targets specific types of computation and builds custom-made protocols, sacrificing generality for efficiency. An important representative of this function-specific approach is an authenticated data structure (ADS), where a specialized protocol is designed that supports query types associated with a particular outsourced dataset. This thesis presents three novel ADS constructions for the important query types of set operations, multi-dimensional range search, and pattern matching, and proves their security under cryptographic assumptions over bilinear groups. The scheme for set operations can support nested queries (e.g., two unions followed by an intersection of the results), extending previous works that only accommodate a single operation. The range search ADS provides an exponential (in the number of attributes in the dataset) asymptotic improvement from previous schemes for storage and computation costs. Finally, the pattern matching ADS supports text pattern and XML path queries with minimal cost, e.g., the overhead at the server is less than 4% compared to simply computing the result, for all our tested settings. The experimental evaluation of all three constructions shows significant improvements in proof-computation time over general-purpose schemes.

Subjects/Keywords: Computer science; Outsourced databases; Secure outsourcing; Verifiable computation

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Papadopoulos, D. (2016). Function-specific schemes for verifiable computation. (Doctoral Dissertation). Boston University. Retrieved from http://hdl.handle.net/2144/19746

Chicago Manual of Style (16th Edition):

Papadopoulos, Dimitrios. “Function-specific schemes for verifiable computation.” 2016. Doctoral Dissertation, Boston University. Accessed December 01, 2020. http://hdl.handle.net/2144/19746.

MLA Handbook (7th Edition):

Papadopoulos, Dimitrios. “Function-specific schemes for verifiable computation.” 2016. Web. 01 Dec 2020.

Vancouver:

Papadopoulos D. Function-specific schemes for verifiable computation. [Internet] [Doctoral dissertation]. Boston University; 2016. [cited 2020 Dec 01]. Available from: http://hdl.handle.net/2144/19746.

Council of Science Editors:

Papadopoulos D. Function-specific schemes for verifiable computation. [Doctoral Dissertation]. Boston University; 2016. Available from: http://hdl.handle.net/2144/19746


University of Arkansas

2. Almakdi, Sultan Ahmed A. Secure and Efficient Models for Retrieving Data from Encrypted Databases in Cloud.

Degree: PhD, 2020, University of Arkansas

Recently, database users have begun to use cloud database services to outsource their databases. The reason for this is the high computation speed and the huge storage capacity that cloud owners provide at low prices. However, despite the attractiveness of the cloud computing environment to database users, privacy issues remain a cause for concern for database owners since data access is out of their control. Encryption is the only way of assuaging users’ fears surrounding data privacy, but executing Structured Query Language (SQL) queries over encrypted data is a challenging task, especially if the data are encrypted by a randomized encryption algorithm. Many researchers have addressed the privacy issues by encrypting the data using deterministic, onion layer, or homomorphic encryption. Nevertheless, even with these systems, the encrypted data can still be subjected to attack. In this research, we first propose an indexing scheme to encode the original table’s tuples into bit vectors (BVs) prior to the encryption. The resulting index is then used to narrow the range of retrieved encrypted records from the cloud to a small set of records that are candidates for the user’s query. Based on the indexing scheme, we then design three different models to execute SQL queries over the encrypted data. The data are encrypted by a single randomized encryption algorithm, namely the Advanced Encryption Standard AES-CBC. In each proposed scheme, we use a different (secure) method for storing and maintaining the index values (BVs) (i.e., either at user’s side or at the cloud server), and we extend each system to support most of relational algebra operators, such as select, join, etc. Implementation and evaluation of the proposed systems reveals that they are practical and efficient at reducing both the computation and space overhead when compared with state-of-the-art systems like CryptDB. Advisors/Committee Members: Brajendra Panda, Susan Gauch, Miaoqing Huang.

Subjects/Keywords: Cloud Databases; Cloud Security; Database Security; Encrypted Databases; Outsourced Databases; Query Processing; Databases and Information Systems; Information Security

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Almakdi, S. A. A. (2020). Secure and Efficient Models for Retrieving Data from Encrypted Databases in Cloud. (Doctoral Dissertation). University of Arkansas. Retrieved from https://scholarworks.uark.edu/etd/3578

Chicago Manual of Style (16th Edition):

Almakdi, Sultan Ahmed A. “Secure and Efficient Models for Retrieving Data from Encrypted Databases in Cloud.” 2020. Doctoral Dissertation, University of Arkansas. Accessed December 01, 2020. https://scholarworks.uark.edu/etd/3578.

MLA Handbook (7th Edition):

Almakdi, Sultan Ahmed A. “Secure and Efficient Models for Retrieving Data from Encrypted Databases in Cloud.” 2020. Web. 01 Dec 2020.

Vancouver:

Almakdi SAA. Secure and Efficient Models for Retrieving Data from Encrypted Databases in Cloud. [Internet] [Doctoral dissertation]. University of Arkansas; 2020. [cited 2020 Dec 01]. Available from: https://scholarworks.uark.edu/etd/3578.

Council of Science Editors:

Almakdi SAA. Secure and Efficient Models for Retrieving Data from Encrypted Databases in Cloud. [Doctoral Dissertation]. University of Arkansas; 2020. Available from: https://scholarworks.uark.edu/etd/3578

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