一种面向中医药数据的高效脱敏算法＊

目的 针对中医药大数据平台传统的加密方案效率不高的问题,提出一种高效的基于属性的内积加密的数据脱敏算法。方法 Hash（哈希）算法是应用广泛的高效的数据加密方法,但传统的哈希算法基于单一的控制策略,效率不高。本文提出一种基于属性的内积加密的数据脱敏算法,把批量的敏感数据分割为不同长度数据颗粒度,与特定密文的哈希进行内积处理。结果 在面对中医药大数据平台的海量数据加密的场景,与传统的哈希加密算法相比,本文提供的加密算法具有很好的性能。结论 为了保障个人隐私数据不被泄露,中医药大数据平台中的个人医疗数据需要加密脱敏后,才能进行分析处理或对外发布。本文提出的算法具备灵活的数据颗粒度、策略和高效的性能表现,适用于海量的中医药数据脱敏。

An Efficient Desensitization Algorithm for Chinese Medicine Data

Objective To propose an efficient data desensitization algorithm based on attribute inner product encryption in view of the low efficiency of traditional encryption scheme of big data platform of traditional Chinese medicine.Methods The hash algorithm is a widely used and efficient data encryption method, but the traditional hash algorithm is based on a single control strategy, and its efficiency is not high. In this paper, a data desensitization algorithm based on attribute inner product encryption was proposed, which divided the batch of sensitive data into different length data granularity and inner product the hash of specific ciphertext.Results Compared with the traditional hash encryption algorithm, this encryption algorithm had a good performance in the face of the massive data encryption scenario of the big data platform of traditional Chinese medicine.Conclusion In order to protect personal privacy data from being leaked, the personal medical data in the big data platform of traditional Chinese medicine needs to be encrypted and desensitized before analyzing and processing or external release. The algorithm proposed in this paper has flexible data granularity, strategy and efficient performance, and is suitable for massive data desensitization.