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。     

Review of Random Phase Encoding in Volume Holographic Storage

Random phase encoding is a unique technique for volume hologram which can be applied to various applications such as holographic multiplexing storage, image encryption, and optical sensing. In this review article, we first review and discuss diffraction selectivity of random phase encoding in volume holograms, which is the most important parameter related to multiplexing capacity of volume holographic storage. We then review an image encryption system based on random phase encoding. The alignment of phase key for decryption of the encoded image stored in holographic memory is analyzed and discussed. In the latter part of the review, an all-optical sensing system implemented by random phase encoding and holographic interconnection is presented.     

基于安全数据交换的HIS综合查询系统设计

目的:为了解决医院数据安全和数据共享之间的矛盾,使患者能够实时地通过多媒体查询平台获得就医体检数据和诊断报告。方法:采用先进的安全数据交换技术达到网络隔离的效果,综合查询系统在硬件架构上采用嵌入式系统,软件使用精简的嵌入式Linux操作系统。结果:通过系统的内网查询模块、外网查询模块和安全数据交换模块的设计,实现了医院信息系统综合查询平台的系统设计。结论:通过在HIS综合查询系统测试平台中的应用,有效地证明了该系统设计方案的可行性、可靠性和实用性。     

Confidential storage and transmission of medical image data

We discuss computationally efficient techniques for confidential storage and transmission of medical image data. Two types of partial encryption techniques based on AES are proposed. The first encrypts a subset of bitplanes of plain image data whereas the second encrypts parts of the JPEG2000 bitstream. We find that encrypting between 20% and 50% of the visual data is sufficient to provide high confidentiality.     

浅谈电子病历的安全控制

信息技术的进步和现代医疗的变革使得电子病历得到越来越广泛的应用。但对于电子病历安全性却没有得到足够的重视。本文从电子病历的数据来源、加密保存、访问控制、网络载体几个方面对电子病历的安全进行探讨，以期对电子病历的发展有所帮助。     

Android平台下居民信息录入系统的设计与实现

目的 解决目前居民信息录入困难导致电子健康档案建立比较缓慢的问题.方法 以Android平台为基础,利用PDA设备进行居民信息录入,结合DES和RSA混合加密算法将数据通过无线网络传输至远程数据接收端,在接收端将数据进行解密存入关系型数据库.结果 对比传统方法,应用本系统可大大节约采集时间,信息的传输速度、安全性和接收率都能满足信息采集的需求.结论 本系统可有效解决居民信息采集过程中工作量大、效率低、出错率高等问题,在实际生活中有重要意义.     

数据库加密技术

在信息化时代来临、Internet高速发展的今天，信息资源的经济和社会价值越来越明显。而数据库系统所管理、存储的数据恰好是各个部门宝贵的信息资源。所以其安全保密性已成为目前所迫切需要解决的问题。本文从数据库安全性需求分析着手，首先对数据库安全与保密作出阐述并重点讨论数据库的加密技术。详细描述了数据库加密的基本流程特点、技术限制、加密对DBMS的影响、加密方式以及数据库的加密方法。     

互联网自助医疗诊断中数据隐私保护方案研究

目的/意义 针对互联网自助医疗诊断中易泄漏患者隐私的痛点,设计一套安全的数据隐私保护方案,既保护患者医疗健康数据,又提供准确的诊断和治疗建议。方法/过程 医疗机构存储的疾病数据和患者医疗健康数据,经同态加密和隐私保护控制技术处理后上传至云服务器,云服务器计算患者医疗健康数据与医疗机构疾病数据之间的相似度并匹配患者疾病情况,将治疗方法发送给患者。结果/结论 基于同态加密和隐私保护访问控制设计的医疗数据隐私保护方案,在互联网自助医疗诊断中能够有效保护患者隐私,同时提供准确的医疗诊断和治疗建议。     

Private predictive analysis on encrypted medical data

Increasingly, confidential medical records are being stored in data centers hosted by hospitals or large companies. As sophisticated algorithms for predictive analysis on medical data continue to be developed, it is likely that, in the future, more and more computation will be done on private patient data. While encryption provides a tool for assuring the privacy of medical information, it limits the functionality for operating on such data. Conventional encryption methods used today provide only very restricted possibilities or none at all to operate on encrypted data without decrypting it first. Homomorphic encryption provides a tool for handling such computations on encrypted data, without decrypting the data, and without even needing the decryption key.In this paper, we discuss possible application scenarios for homomorphic encryption in order to ensure privacy of sensitive medical data. We describe how to privately conduct predictive analysis tasks on encrypted data using homomorphic encryption. As a proof of concept, we present a working implementation of a prediction service running in the cloud (hosted on Microsoft’s Windows Azure), which takes as input private encrypted health data, and returns the probability for suffering cardiovascular disease in encrypted form. Since the cloud service uses homomorphic encryption, it makes this prediction while handling only encrypted data, learning nothing about the submitted confidential medical data.