信息网络安全

信息网络安全 ›› 2019, Vol. 19 ›› Issue (1): 68-75.doi: 10.3969/j.issn.1671-1122.2019.01.009

• 技术研究 • 上一篇    下一篇

大数据平台下应用程序保护机制的研究与实现

吴天雄1,2(), 陈兴蜀2, 罗永刚2   

  1. 1.四川大学计算机学院,四川成都 610065
    2.四川大学网络空间安全研究院,四川成都 610065
  • 收稿日期:2018-08-15 出版日期:2019-01-20 发布日期:2020-05-11
  • 作者简介:

    作者简介：吴天雄（1994—）,男,湖北,硕士研究生,主要研究方向为大数据和信息安全;陈兴蜀（1968—）,女,四川,教授,博士,主要研究方向为网络安全、云计算与大数据;罗永刚（1980—）,男,贵州,博士研究生,主要研究方向为大数据和网络安全。

  • 基金资助:
    国家自然科学基金[61272447];国家“双创”示范基地之变革性技术国际研发转化平台资助项目[C700011]

Research and Implementation of Application Program Protection Mechanism under Big Data Platform

Tianxiong WU1,2(), Xingshu CHEN2, Yonggang LUO2   

  1. 1.School of Computer, Sichuan University, Chengdu Sichuan 610065, China
    2.Cybersecurity Research Institute, Sichuan University, Chengdu Sichuan 610065, China
  • Received:2018-08-15 Online:2019-01-20 Published:2020-05-11

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摘要：

近年来,大数据产业呈现爆炸的增长趋势,人们认识到数据对生产的重要价值,与此同时,产生了各种各样对大数据进行分析和挖掘的平台。但由于目前流行的大数据处理框架如Hadoop、Spark都是基于Java字节码机制编译的,用户所编写的应用程序可以被完全地反编译,应用程序的核心思想也就直接暴露。基于此,文章设计了一个完整的针对大数据平台下的用户应用程序保护的解决方案,该方案由加密模块、分布式解密模块和分布式过滤模块组成。文章提出的大数据平台下应用程序保护机制借鉴了单机下的代码保护机制,结合大数据平台计算引擎的工作流程、工作特点进行了深度整合。通过实验测试和实际应用,文章提出的解决方案可以实现大数据平台下应用程序保护,且该方案几乎不会影响应用程序的运行性能。

关键词: 大数据平台, 代码保护, 分布式解密, Hadoop, Spark

Abstract:

In recent years, the big data industry has shown an explosive growth trend. People have realized the importance of data for production. At the same time, various platforms have been created to help analyze and mine big data, but due to the current popularity Data processing frameworks such as Hadoop and Spark are based on the Java bytecode mechanism, so that applications written by users can be completely decompiled, and the core ideas of applications are directly exposed. Based on this, this paper designs a complete solution for user application protection under the big data platform, which consists of a cryptographic module, a distributed decryption module, and a distributed filtering module. The application protection mechanism proposed in the article under the big data platform draws on the code protection mechanism under the single machine and combines the work flow and work characteristics of the big data platform computing engine. Through experimental testing and practical application, the solution proposed in this paper can achieve application protection under the big data platform, and the program will hardly affect the running performance of the application.

Key words: big data platform, code protection, distributed decryption, Hadoop, Spark

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