Gene data desensitization method for realizing efficient similarity query and access control

Abstract

The invention belongs to the technical field of information security. The invention particularly provides a gene data desensitization method for realizing efficient similarity query and access control. The similarity query of large-scale gene data in a ciphertext environment is effectively supported; meanwhile, complex logic query is supported to meet the personalized query requirement of the user; according to the method, the authorization mode is flexible, different access authorities can be given to different data, reliable control over the data access authority of a user is achieved in the query process, in addition, a specific Hash function is adopted for compressing the data, the number of matched elements in the ciphertext state is remarkably reduced, and the query retrieval efficiency is further improved.

Description

technical field [0001] The invention belongs to the technical field of information security, and in particular relates to a genetic data desensitization method for realizing efficient similarity query and access control. Background technique [0002] Gene similarity query has become a hot research field with good prospects for medical and health applications. For example, disease prediction, through early preventive treatment to reduce the incidence of people with similar genes to the disease. With the advent of the big data era, the huge amount of genetic data and the computational overhead of similarity queries between genes have led more and more companies and enterprises to outsource local genetic data to third-party cloud servers, such as DNAnexus, Google Genomics, etc. . However, by directly outsourcing genetic data to cloud servers in clear text, cloud service providers can easily obtain user sensitive information such as gene sequences and health status, which pose...

AI Technical Summary

Problems solved by technology

However, by directly outsourcing genetic data to cloud servers in clear text, cloud service providers can easily obtain user sensitive information such as gene sequences, health status, etc., which poses a great risk of leakage of user privacy data.

[0003] To solve the above problems, a simple way is to use traditional encryption methods (such as AES) to encrypt the entire genetic data before outsourcing the genetic data to the cloud server; however, this will significantly reduce the availability of data, because the cloud server cannot Perform genetic similarity query in ciphertext state; therefore, it is urgent to design a safe, efficient and fully functional genetic data desensitization scheme

[0004] At present, the existing genetic data desensitization schemes only consider the query efficiency in the ciphertext environment, while ignoring many important factors, especially data access control and personalized query requirements (such as complex logical queries); in practical applications In the scenario, different users may have different access rights to genetic data, and users may only pay attention to some specific gene fragments on the gene sequence instead of the entire sequence when querying, which requires that the desensitization scheme should have comprehensive access control mechanism and can provide fine-grained ciphertext query and retrieval methods

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