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PP-DDP: a privacy-preserving outsourcing framework for solving the double digest problem by Jingwen Suo; Lize Gu; Xingyu Yan; Sijia Yang; Xiaoya Hu; Licheng Wang is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

What is PP-DDP: a privacy-preserving outsourcing framework for solving the double digest problem about?

Abstract Background As one of the fundamental problems in bioinformatics, the double digest problem (DDP) focuses on reordering genetic fragments in a proper sequence. Although many algorithms for dealing with the DDP problem were proposed during the past decades, it is believed that solving DDP is still very time-consuming work due to the strongly NP-completeness of DDP. However, none of these algorithms consider the privacy issue of the DDP data that contains critical business interests and is collected with days or even months of gel-electrophoresis experiments. Thus, the DDP data owners are reluctant to deploy the task of solving DDP over cloud. Results Our main motivation in this paper is to design a secure outsourcing computation framework for solving the DDP problem. We at first propose a privacy-preserving outsourcing framework for handling the DDP problem by using a cloud server; Then, to enable the cloud server to solve the DDP instances over ciphertexts, an order-preserving homomorphic index scheme (OPHI) is tailored from an order-preserving encryption scheme published at CCS 2012; And finally, our previous work on solving DDP problem, a quantum inspired genetic algorith

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Author

Jingwen Suo; Lize Gu; Xingyu Yan; Sijia Yang; Xiaoya Hu; Licheng Wang

Publisher

Springer Science and Business Media LLC

Published

2023

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)