Privacy-Preserving Regular Expression Evaluation on Encrypted Data Public Deposited

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  • March 22, 2019
Creator
  • Wei, Lei
    • Affiliation: College of Arts and Sciences, Department of Computer Science
Abstract
  • Motivated by the need to outsource file storage to untrusted clouds while still permitting controlled use of that data by authorized third parties, in this dissertation we present a family of protocols by which a client can evaluate a regular expression on an encrypted file stored at a server (the cloud), once authorized to do so by the file owner. We present a protocol that provably protects the privacy of the regular expression and the file contents from a malicious server and the privacy of the file contents (except for the evaluation result) from an honest-but-curious client. We then extend this protocol in two primary directions. In one direction, we develop a strengthened protocol that enables the client to detect any misbehavior of the server; in particular, the client can verify that the result of its regular-expression evaluation is based on the authentic file stored there by the data owner, and in this sense the file and evaluation result are authenticated to the client. The second direction in which we extend our initial protocol is motivated by the vast adoption of resource-constrained mobile devices, and the fact that our protocols involve relatively intensive client-server interaction and computation on the searching client. We therefore investigate an alternative in which the client (e.g., via her mobile device) can submit her encrypted regular expression to a partially trusted proxy, which then interacts with the server hosting the encrypted data and reports the encrypted evaluation result to the client. Neither the search query nor the result is revealed to an honest-but-curious proxy or malicious server during the process. We demonstrate the practicality of the protocol by prototyping a system to perform regular-expression searches on encrypted emails and evaluate its performance using a real-world email dataset.
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  • In Copyright
Advisor
  • Reiter, Michael
Degree
  • Doctor of Philosophy
Graduation year
  • 2013
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