大数据平台下应用程序保护机制的研究与实现
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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。
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.
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.
引文
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[2]ZAHARIA M,XIN R S,WENDLL P,et al.Apache Spark:a Unified Engine for Big Data Processing[J].Communications of the Acm,2016,59(11):56-65.
[3]GONG Yaming.Reinforcement Method and System for Jar Packet:China,CN 106845169 A[P].2017-6-13.
[4]ZHAO Hong,GUO Nan.Java Source Code Protection Based on JNI and AES Algorithm[C]//IEEE.2017 IEEE International Conference on Computational Science and Engineering(CSE)and IEEE International Conference on Embedded and Ubiquitous Computing(EUC),July 21-24,2017,Guangzhou,China.New York:IEEE,2017:846-849.
[5]FENG Dengguo.Computer Communication Network Security[M].Beijing:Tsinghua University Press,2001.冯登国.计算机通信网络安全[M].北京:清华大学出版社,2001.
[6]LI Chunhua,WANG Hua,ZHANG Yanzhe,et al.Optimization for Broadcast Encryption in Cloud Using Extended Public Key[J].Journal of Computer Research and Development,2017,54(12):2741-2747.
[7]SONG Jianye,HE Nuan,ZHU Yiming,et al.Design and Implementation of Secure Deduplication System for Ciphertext Data Based on Aliyun[J].Netinfo Security,2017(3):39-45.宋建业,何暖,朱一明,等.基于阿里云平台的密文数据安全去重系统的设计与实现[J].信息网络安全,2017,17(3):39-45.
[8]Tim Lindholm.Java Virtual Machine Specification[M].Beijing:China Machine Press,2014.
[9]ZHOU Zhiming.Understanding the JVM[M].Beijing:China Machine Press,2013.周志明.深入理解Java虚拟机[M].北京:机械工业出版社,2013.
[10]DONG Y,REN K,WANG SY,et al.Construction and Certif ication of a Bytecode Virtual Machine[J].Journal of Software,2010,21(2):305-317.
[11]MA Junyan,ZENG Guosun.Research and Implementation on Parallel Crawl Method for Source Code Based on MapReduce[J].Netinfo Security,2018,18(1):58-66.马军岩,曾国荪.基于MapReduce的开源代码并行爬取方法研究与实现[J].信息网络安全,2018,18(1):58-66.
[12]YING Changtian,YU Jiong,BIAN Chen,et al.Criticality Checkpoint Management Strategy Based on RDD Characteristics in Spark.Journal of Computer Research and Development,2017,54(12):2858-2872.
[13]LIU Haiqing.DPA Resistant Hardware Implementation of AESBased on Masking[D].Changsha:National University of Defense Technology,2008.刘海清.基于随机掩码的AES算法抗DPA攻击硬件实现[D].长沙:国防科技大学,2008.
[14]FEI Liming,ZHENG Xiaojuan,YANG Zhicai,et al.JVMTI-based Implementation of Security Policies Enforcement on java Platform[C]//IEEE.International Conference on Advanced Computer Theory&Engineering,Auguest 20-22,2010,Chengdu China.New York:IEEE,2010:210-213.
[15]WHITE T.Hadoop:The Definitive Guide[M].California:O'Reilly Media,2012.