Efficient and Secure Count Queries on Encrypted Genomic Data

Human Genomics research has gained certain attention since these studies provide an efficient way to identify potential relationships between complex diseases and certain genes, thus making medical decisions more rational and effective. However, implementing such studies usually requires the aggregation of data from multiple organizations into a large database. In view of the emergence of various privacy issues and other security requirements, there is a need for a secure data querying method to ensure correct querying of the database without compromising the privacy of the participants. This paper presents a method to achieve efficient gene data querying, which not only protects the privacy of participants but also effectively deals with storage and computational issues. In particular, there are three main contributions. Firstly, this paper introduces a new data structure, a hash tree, that improves query performance and decreases communication costs by utilizing hash functions. Secondly, to ensure the accuracy of search results, we combined Bloom filter feedback with an added table. Thirdly, to maintain privacy in the query process, we propose a data comparison protocol based on Benaloh encryption. Moreover, our experiment shows higher efficiency than previous schemes in time cost and communication cost.