Privacy-Preserving Search for a Similar Genomic Makeup in the Cloud

by   Xiaojie Zhu, et al.

In this paper, we attempt to provide a privacy-preserving and efficient solution for the "similar patient search" problem among several parties (e.g., hospitals) by addressing the shortcomings of previous attempts. We consider a scenario in which each hospital has its own genomic dataset and the goal of a physician (or researcher) is to search for a patient similar to a given one (based on a genomic makeup) among all the hospitals in the system. To enable this search, we let each hospital encrypt its dataset with its own key and outsource the storage of its dataset to a public cloud. The physician can get authorization from multiple hospitals and send a query to the cloud, which efficiently performs the search across authorized hospitals using a privacy-preserving index structure. We propose a hierarchical index structure to index each hospital's dataset with low memory requirements. Furthermore, we develop a novel privacy-preserving index merging mechanism that generates a common search index from individual indices of each hospital to significantly improve the search efficiency. We also consider the storage of medical information associated with genomic data of a patient (e.g., diagnosis and treatment). We allow access to this information via a fine-grained access control policy that we develop through the combination of standard symmetric encryption and ciphertext policy attribute-based encryption. Using this mechanism, a physician can search for similar patients and obtain medical information about the matching records if the access policy holds. We conduct experiments on large-scale genomic data and show the efficiency of the proposed scheme. Notably, we show that under our experimental settings, the proposed scheme is more than 60 times faster than Wang et al.'s protocol and 97 times faster than Asharov et al.'s solution.



There are no comments yet.


page 1

page 5


Privacy-Preserving Identification of Target Patients from Outsourced Patient Data

With the increasing affordability and availability of patient data, hosp...

ESAS: An Efficient Semantic and Authorized Search Scheme over Encrypted Outsourced Data

Nowadays, a large amount of user privacy-sensitive data is outsourced to...

Scalar Product Lattice Computation for Efficient Privacy-preserving Systems

Privacy-preserving applications allow users to perform on-line daily act...

Achieve Efficient Position-Heap-based Privacy-Preserving Substring-of-Keyword Query over Cloud

The cloud computing technique, which was initially used to mitigate the ...

Privacy-preserving Medical Treatment System through Nondeterministic Finite Automata

In this paper, we propose a privacy-preserving medical treatment system ...

Obfuscated Access and Search Patterns in Searchable Encryption

Searchable Symmetric Encryption (SSE) allows a data owner to securely ou...

Privacy-Preserving and Communication-Efficient Causal Inference for Hospital Quality Measurement

Data sharing can improve hospital quality measurement, but sharing patie...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.