PPMT: Privacy-Preserving Genomic Data Sharing with Personalized Medicine Testing in Cloud Computing
首发时间:2020-01-16
Abstract:With the rapid development of bioinformatics and the availability of genetic sequencing technologies, genomic data ushers in a new era of precision medicine. Cloud computing, features as low cost, rich storage and rapid processing can precisely respond to the challenges brought by the emergence of massive genomic data. Considering the security of cloud platform and the privacy of genomic data, we firstly introduce PPMT which utilizes key-policy attribute-based encryption (KP-ABE) to realize genomic data access control with abundant attributes, and employs KP-ABE with equality test to achieve personalized medicine test by matching digitized single nucleotide polymorphisms (SNPs) directly on the users' ciphertext without encrypting multiple times. We conduct extensive experiments with the dataset ``1000 Genomes", and the results show that PPMT can greatly reduce the computation and communication overhead compared with existing schemes and are practical enough test authorization requirements.
keywords: Genomic privacy access control attribute-based encryption equality test personalized medicine test
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云环境中支持精准医疗测试的基因数据安全共享方案
摘要:随着生物信息学的飞速发展和基因测序技术的广泛应用,基因数据开创了精密医学的新纪元。云计算具有低成本,丰富的存储和快速处理的功能,可以有效地应对海量基因组数据的出现所带来的挑战。考虑到云平台的安全性和基因组数据的私密性,首先基于密钥策略的属性加密算法(KP-ABE)实现基因数据的细粒度访问控制,并结合等值测试算法进行密文上数字化的单核苷酸多态性(SNP)匹配构造PPMT方案,实现个性化的精准医疗测试。利用千人组基因数据集进行了广泛的实验,结果表明与现有方案相比,PPMT方案可有效减少计算和通信开销,具有实用性。
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