Research on Intelligent Security Protection of Privacy Data in Government Cyberspace
| Title | Research on Intelligent Security Protection of Privacy Data in Government Cyberspace |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Yang, H., Huang, L., Luo, C., Yu, Q. |
| Conference Name | 2020 IEEE 5th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA) |
| Date Published | April 2020 |
| Publisher | IEEE |
| ISBN Number | 978-1-7281-6024-5 |
| Keywords | agent-based intelligent masking middleware, automatic execution, automatic recommendation, big data privacy, categorization and classification, Classification algorithms, Data analysis, data masking algorithm, Data models, data privacy, data source, dynamic masking, Government, government cyberspace, government data masking process, government data processing, government privacy data, government unstructured text, Human Behavior, improved masking process, intelligent discovery, intelligent security protection, learning (artificial intelligence), machine learning, Metrics, middleware, NLP, open systems, privacy protection, protection efficiency, pubcrawl, resilience, Resiliency, Scalability, security, sensitive data discovery, static masking, structured privacy data, unstructured privacy data |
| Abstract | Based on the analysis of the difficulties and pain points of privacy protection in the opening and sharing of government data, this paper proposes a new method for intelligent discovery and protection of structured and unstructured privacy data. Based on the improvement of the existing government data masking process, this method introduces the technologies of NLP and machine learning, studies the intelligent discovery of sensitive data, the automatic recommendation of masking algorithm and the full automatic execution following the improved masking process. In addition, the dynamic masking and static masking prototype with text and database as data source are designed and implemented with agent-based intelligent masking middleware. The results show that the recognition range and protection efficiency of government privacy data, especially government unstructured text have been significantly improved. |
| URL | https://ieeexplore.ieee.org/document/9095637 |
| DOI | 10.1109/ICCCBDA49378.2020.9095637 |
| Citation Key | yang_research_2020 |
- protection efficiency
- intelligent discovery
- intelligent security protection
- learning (artificial intelligence)
- machine learning
- Metrics
- middleware
- NLP
- open systems
- privacy protection
- improved masking process
- pubcrawl
- resilience
- Resiliency
- Scalability
- security
- sensitive data discovery
- static masking
- structured privacy data
- unstructured privacy data
- data source
- automatic execution
- automatic recommendation
- big data privacy
- categorization and classification
- Classification algorithms
- data analysis
- data masking algorithm
- Data models
- data privacy
- agent-based intelligent masking middleware
- dynamic masking
- Government
- government cyberspace
- government data masking process
- government data processing
- government privacy data
- government unstructured text
- Human behavior