Big Data Research

Techniques of big data security from the perspective of life cycle management

Shudong LI, Yan JIA, Xiaobo WU, Aiping LI, Xiaodong YANG, Dawei ZHAO

2017, 3(5): 3-19. doi:10.11959/j.issn.2096-0271.2017047

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The big data security problems during the process of data production,storage,use,transmission,sharing and destruction have been explored.Firstly,the development strategies of big data security worldwide were summarized.Second,from the perspective of the whole life cycle management,the current technical research and the progress of big data security protection were introduced by focusing on its collection,storage,transport,use and open sharing,destruction and management strategy.Finally,some new problems that need to be solved in the future research of big data security were discussed.

Personal information de-identification architecture and standardization

Anming XIE, Tao JIN, Tao ZHOU

2017, 3(5): 20-29. doi:10.11959/j.issn.2096-0271.2017048

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With the development of big data,there are many difficulties to protect personal information.De-identification removes identifiable information from a dataset so that individual data cannot be linked with specific individuals.De-identification thus helps to balance the contradictory goals of sharing personal information while protecting privacy.De-identification architecture on personal information was proposed.The specifications on implementing a de-identification process were described.On the basis of big data security standard system,some suggestions were proposed to develop de-identification standards.

Security capability practice of big data

Yuejin DU, Bin ZHENG

2017, 3(5): 30-37. doi:10.11959/j.issn.2096-0271.2017049

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The purpose of security is to ensure development,and it is important to measure the data security capability of an organization.The security problems and challenges of data organization were discussed.The development trend of big data and data security capability framework were introduced.The path of data security capability implementation and difficulties in the process of practice were expounded.Take an internet financial enterprise as an example,the process and method of building data security capability by using data security capability maturity model were analyzed.

Practice on security of big data platform

Jie LIU

2017, 3(5): 38-44. doi:10.11959/j.issn.2096-0271.2017050

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The security mechanism of open source platform is not sophisticated,especially on user authentication and log audit.The potential safety problems of open source platform were analyzed.Based on the open source Hadoop platform,the user authentication based on Giano,and the efficient log audit based on rules were proposed.Combined with the practical application of Baidu,the effectiveness of the technical scheme was expounded.The technical scheme has certain universality,and it can be flexibly used according to the current technological environment of the enterprise.

Privacy preserving in the age of big data

Xianjin FANG, Yafei XIAO, Gaoming YANG

2017, 3(5): 45-56. doi:10.11959/j.issn.2096-0271.2017051

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One of biggest concerns of big data is privacy,especially,in the processing of big data publishing or data mining.However,the study on big data privacy is still at a very early stage.The preliminary knowledge about the definition of roles and operations of privacy system were introduced.The mathematical description and measurement metrics of privacy study was given.The models of privacy preserving were analyzed,including k-anonymity and differential privacy.The current situation of privacy preserving in big data age was reviewed,especially,the privacy preserving based location-based services and its applications were summarized.The challenges and opportunities in the age of big data were summarized.The directions to improve the existing privacy protection methods satisfying the unprecedented computational requirements of big data were pointed out.

Crowdsensing big data:sensing,data selection,and understanding

Bin GUO, Shuying ZHAI, Zhiwen YU, Xingshe ZHOU

2017, 3(5): 57-69. doi:10.11959/j.issn.2096-0271.2017052

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Mobile crowdsensing (MCS) has become an emerging paradigm for large-scale sensing.It empowers ordinary citizens to contribute data sensed or generated from their mobile devices (e.g.,smartphones,wearable devices),aggregates and fuses the data in the cloud for crowd intelligence extraction and human-centric service delivery.The data contributed by the crowd in MCS systems presents the features such as multi-modal,rich-content,spatio-temporal,and human-centric.The key challenges and techniques about crowdsensing big data were discussed.The recent progress of our group in this promising research area was described.

Big data storage management based on new storage

Peiquan JIN

2017, 3(5): 70-82. doi:10.11959/j.issn.2096-0271.2017053

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How to efficiently store big data and support real-time big data processing and analysis has been the most critical issue in the development of big data technologies.Recently,new storage media such as phase change memory and flash memory provides new opportunities for developing an efficient framework for big data storage and management.Based on the challenges of efficient storage and real-time processing in big data storage and management,storage class memories were focused,which were represented by phase change memory,and the state of the art of new-storage-based big data storage management was discussed.Finally,some future research directions for new-storage-based big data storage management were proposed.

Process and methods of clinical big data mining based on electronic medical records

Tong RUAN, Ju GAO, Donglei FENG, Xiyuan QIAN, Ting WANG, Chenglin SUN

2017, 3(5): 83-98. doi:10.11959/j.issn.2096-0271.2017054

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Electronic medical records from hospitals record the patient's disease,diagnosis and treatment information.It forms the basis of clinical data.Mining such data can assist doctors in clinical research and clinical diagnosis and treatment.Firstly,challenges encountered in the process of big data mining on EMR were raised,then the core process was summarized.The process includes tasks such as clinical data integration,the construction of clinical specialist disease database based on knowledge graph,the quality assessment methods on EMR,and comparative effectiveness and risk prediction of diseases as the core of clinical big data applications.A solution for each task was proposed,and the experimental results were given.Finally,the future directions of technologies and applications of big data mining on healthcare were presented.

Evaluation method for regional development level of big data

Hui AN, Jinxin ZHUANG, Yiming LI

2017, 3(5): 99-105. doi:10.11959/j.issn.2096-0271.2017055

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Implementing the national big data strategy and promoting the development and application of big data,is becoming the focus of the work of local governments in China.And there is an urgent need for specific guidance with pertinence and operability.The significance of research on evaluation method for regional development level of big data was explained,the difficulties faced in the evaluation were analyzed,innovative solutions were designed,the overall design of index system and evaluation mechanism evaluation method was proposed,and the direction and the focus for further work was put forward.

Uncovering the illegal pyramid networks by big data

2017, 3(5): 106-112. doi:10.11959/j.issn.2096-0271.2017056

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