Data Sensitivity and Classification Management: A Declarative Approach

Yuejin Zhang; Hong Liu; Guowei Wang

doi:doi:10.11648/j.ijics.20210603.12

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Data Sensitivity and Classification Management: A Declarative Approach

Yuejin Zhang, Hong Liu, Guowei Wang

Published in International Journal of Information and Communication Sciences (Volume 6, Issue 3)

Received: 12 July 2021 Accepted: 30 July 2021 Published: 9 August 2021

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Abstract

Data protection according to sensitivity and classification has become a mandatory security mechanism for safety- and security-critical organizations. There is however no consensus on how to implement data sensitivity and classification in existing big-data systems. An approach is proposed to express and compute data sensitivity and multidimensional data classification in fine granularity. The approach is based on a declarative logic programming language, which is able to separate security requirement definitions and deduction from implementation details. Expressing and validating the security rules can be done transparently, ignoring underlying technical migrations and infrastructure differences. It is therefore possible to use the same set of security rules among various big data systems. Compared to other logic-programming-based approach, the declarative nature also makes it preferable for modular development and system maintenance. Sensitivity specification is shown and security analysis including conflict detection and resolution is performed on the same platform. Several typical types of data classification have also been illustrated and analyzed. The approach is capable of expressing complex classification methods, including classification with multiple parameters, classification according to graph computation, and classification based on relations among multiple data objects. The logic programming-based method is shown to have more expressive power and better complexity performance than conventional methods.

Published in	International Journal of Information and Communication Sciences (Volume 6, Issue 3)
DOI	10.11648/j.ijics.20210603.12
Page(s)	62-65
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Data Security, Sensitivity and Classification, Logic Programming, Big Data

References

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[4]	B. Motik; Y. Nenov; R. Piro, I. Horrocks, Parallel Materialisation of Datalog Programs in Main-Memory RDF Databases, In Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, Qébec City, Qébec, Canada: 2014.
[5]	Alexander Shkapsky, Mohan Yang, Matteo Interlandi, Hsuan Chiu, Tyson Condie, Carlo Zaniolo, Big Data Analytics with Datalog Queries on Spark, SIGMOD’16, San Francisco, CA, USA: 2016.
[6]	Jiaqi Gu, Yugo Watanabe, William Mazza, Alexander Shkapsky, Mohan Yang, Ling Ding, Carlo Zaniolo, RaSQL: Greater Power and Performance for Big Data Analytics with Recursive-aggregate-SQL on Spark, SIGMOD’19, 2019.
[7]	Grigoris Antoniou, Sotiris Batsakis, Raghava Mutharaju, Jeff Z. Pan, Guilin Qi, Ilias Tachmazidis, Jacopo Urbani and Zhangquan Zhou, A Survey of Large-Scale Reasoning on the Web of Data, The Knowledge Engineering Review, Vol. 33, 1–24, 2018.
[8]	Boris Motik, Yavor Nenov, Robert Piro, Ian Horrocks, Dan Olteanu, Parallel Materialisation of Datalog Programs in Centralised, Main-Memory RDF Systems, AAAI, 2014.
[9]	Mohan Yang, Declarative Languages and Scalable Systems for Graph Analytics and Knowledge Discovery, PhD dissertation, University of California, Los Angeles, 2017.
[10]	Carlo Zaniolo, Mohan Yang, Matteo Interlandi, Ariyam Das, Alexander Shkapsky, Tyson Condie. Fixpoint semantics and optimization of recursive Datalog programs with aggregates. TPLP 17 (5-6), 2017, 1048-1065.
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[13]	Wang, J., Wu, J., Li, M. et al. Formal semantics and high performance in declarative machine learning using Datalog. The VLDB Journal, 2021.
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[15]	Xinming Ou, A logic-programming approach to network security analysis, PhD dissertation, Princeton University, 2005.

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APA Style

Yuejin Zhang, Hong Liu, Guowei Wang. (2021). Data Sensitivity and Classification Management: A Declarative Approach. International Journal of Information and Communication Sciences, 6(3), 62-65. https://doi.org/10.11648/j.ijics.20210603.12

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ACS Style

Yuejin Zhang; Hong Liu; Guowei Wang. Data Sensitivity and Classification Management: A Declarative Approach. Int. J. Inf. Commun. Sci. 2021, 6(3), 62-65. doi: 10.11648/j.ijics.20210603.12

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AMA Style

Yuejin Zhang, Hong Liu, Guowei Wang. Data Sensitivity and Classification Management: A Declarative Approach. Int J Inf Commun Sci. 2021;6(3):62-65. doi: 10.11648/j.ijics.20210603.12

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@article{10.11648/j.ijics.20210603.12,
  author = {Yuejin Zhang and Hong Liu and Guowei Wang},
  title = {Data Sensitivity and Classification Management: A Declarative Approach},
  journal = {International Journal of Information and Communication Sciences},
  volume = {6},
  number = {3},
  pages = {62-65},
  doi = {10.11648/j.ijics.20210603.12},
  url = {https://doi.org/10.11648/j.ijics.20210603.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijics.20210603.12},
  abstract = {Data protection according to sensitivity and classification has become a mandatory security mechanism for safety- and security-critical organizations. There is however no consensus on how to implement data sensitivity and classification in existing big-data systems. An approach is proposed to express and compute data sensitivity and multidimensional data classification in fine granularity. The approach is based on a declarative logic programming language, which is able to separate security requirement definitions and deduction from implementation details. Expressing and validating the security rules can be done transparently, ignoring underlying technical migrations and infrastructure differences. It is therefore possible to use the same set of security rules among various big data systems. Compared to other logic-programming-based approach, the declarative nature also makes it preferable for modular development and system maintenance. Sensitivity specification is shown and security analysis including conflict detection and resolution is performed on the same platform. Several typical types of data classification have also been illustrated and analyzed. The approach is capable of expressing complex classification methods, including classification with multiple parameters, classification according to graph computation, and classification based on relations among multiple data objects. The logic programming-based method is shown to have more expressive power and better complexity performance than conventional methods.},
 year = {2021}
}

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TY - JOUR
T1 - Data Sensitivity and Classification Management: A Declarative Approach
AU - Yuejin Zhang
AU - Hong Liu
AU - Guowei Wang
Y1 - 2021/08/09
PY - 2021
N1 - https://doi.org/10.11648/j.ijics.20210603.12
DO - 10.11648/j.ijics.20210603.12
T2 - International Journal of Information and Communication Sciences
JF - International Journal of Information and Communication Sciences
JO - International Journal of Information and Communication Sciences
SP - 62
EP - 65
PB - Science Publishing Group
SN - 2575-1719
UR - https://doi.org/10.11648/j.ijics.20210603.12
AB - Data protection according to sensitivity and classification has become a mandatory security mechanism for safety- and security-critical organizations. There is however no consensus on how to implement data sensitivity and classification in existing big-data systems. An approach is proposed to express and compute data sensitivity and multidimensional data classification in fine granularity. The approach is based on a declarative logic programming language, which is able to separate security requirement definitions and deduction from implementation details. Expressing and validating the security rules can be done transparently, ignoring underlying technical migrations and infrastructure differences. It is therefore possible to use the same set of security rules among various big data systems. Compared to other logic-programming-based approach, the declarative nature also makes it preferable for modular development and system maintenance. Sensitivity specification is shown and security analysis including conflict detection and resolution is performed on the same platform. Several typical types of data classification have also been illustrated and analyzed. The approach is capable of expressing complex classification methods, including classification with multiple parameters, classification according to graph computation, and classification based on relations among multiple data objects. The logic programming-based method is shown to have more expressive power and better complexity performance than conventional methods.
VL - 6
IS - 3
ER -

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Author Information

Yuejin Zhang

Department of Electrical Engineering, Smart City College, Beijing Union University, Beijing, China
Hong Liu

Run Technologies Co., Ltd. Beijing, Beijing, China
Guowei Wang

Beijing Municipal Bureau of Public Security, Beijing, China

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APA Style

Yuejin Zhang, Hong Liu, Guowei Wang. (2021). Data Sensitivity and Classification Management: A Declarative Approach. International Journal of Information and Communication Sciences, 6(3), 62-65. https://doi.org/10.11648/j.ijics.20210603.12

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ACS Style

Yuejin Zhang; Hong Liu; Guowei Wang. Data Sensitivity and Classification Management: A Declarative Approach. Int. J. Inf. Commun. Sci. 2021, 6(3), 62-65. doi: 10.11648/j.ijics.20210603.12

Copy | Download

AMA Style

Yuejin Zhang, Hong Liu, Guowei Wang. Data Sensitivity and Classification Management: A Declarative Approach. Int J Inf Commun Sci. 2021;6(3):62-65. doi: 10.11648/j.ijics.20210603.12

Copy | Download

@article{10.11648/j.ijics.20210603.12,
  author = {Yuejin Zhang and Hong Liu and Guowei Wang},
  title = {Data Sensitivity and Classification Management: A Declarative Approach},
  journal = {International Journal of Information and Communication Sciences},
  volume = {6},
  number = {3},
  pages = {62-65},
  doi = {10.11648/j.ijics.20210603.12},
  url = {https://doi.org/10.11648/j.ijics.20210603.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijics.20210603.12},
  abstract = {Data protection according to sensitivity and classification has become a mandatory security mechanism for safety- and security-critical organizations. There is however no consensus on how to implement data sensitivity and classification in existing big-data systems. An approach is proposed to express and compute data sensitivity and multidimensional data classification in fine granularity. The approach is based on a declarative logic programming language, which is able to separate security requirement definitions and deduction from implementation details. Expressing and validating the security rules can be done transparently, ignoring underlying technical migrations and infrastructure differences. It is therefore possible to use the same set of security rules among various big data systems. Compared to other logic-programming-based approach, the declarative nature also makes it preferable for modular development and system maintenance. Sensitivity specification is shown and security analysis including conflict detection and resolution is performed on the same platform. Several typical types of data classification have also been illustrated and analyzed. The approach is capable of expressing complex classification methods, including classification with multiple parameters, classification according to graph computation, and classification based on relations among multiple data objects. The logic programming-based method is shown to have more expressive power and better complexity performance than conventional methods.},
 year = {2021}
}

Copy | Download