A Corruption-resistant Data Identification Technology Based on Dataset Honeypoint

Li Haobo; Li Mohan; Chen Peng; Sun Yanbin; Tian Zhihong

doi:10.7544/issn1000-1239.202440496

Journal of Computer Research and Development > 2024 > 61(10): 2417-2432. > DOI: 10.7544/issn1000-1239.202440496 CSTR: 32373.14.issn1000-1239.202440496

Li Haobo, Li Mohan, Chen Peng, Sun Yanbin, Tian Zhihong. A Corruption-resistant Data Identification Technology Based on Dataset Honeypoint[J]. Journal of Computer Research and Development, 2024, 61(10): 2417-2432. DOI: 10.7544/issn1000-1239.202440496

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A Corruption-resistant Data Identification Technology Based on Dataset Honeypoint

Li Haobo^1,,
Li Mohan^1, ,,
Chen Peng¹,
Sun Yanbin^{1, 2},
Tian Zhihong^{1, 2}

1.
Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006
2.
Guangdong Provincial Key Laboratory of Industrial Control System Security, Guangzhou 510006

Funds: This work was supported by the National Key Research and Development Program of China (2021YFB3101704), the National Natural Science Foundation of China (62372126, 62272119, 62072130, U20B2046), the Guangdong Basic and Applied Basic Research Foundation (2023A1515030142), the Guangzhou Basic and Applied Basic Research Foundation (2024A04J9969), the Guangzhou University Project (YJ2023047), Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme (2019), the Guangdong Higher Education Innovation Group Project (2020KCXTD007), and the Strategic Research and Consulting Project of the Chinese Academy of Engineering (2023-JB-13).

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Author Bio:
Li Haobo: born in 2000. Master candidate. Student member of CCF. His main research interests include data security and deception defense

Li Mohan: born in 1987. PhD, professor. Senior member of CCF. Her main research interests include AI security, data governance, and intrusion detection

Chen Peng: born in 1988. PhD, postdoc. Member of CCF. His main research interests include explainable AI, shape manifold analysis, and data governance

Sun Yanbin: born in 1987. PhD, professor. Senior member of CCF. His main research interests include network security, industrial control security, future network, and data governance

Tian Zhihong: born in 1978. PhD, professor, PhD supervisor. Distinguished member of CCF. His main research interests include network attack and defense confrontation, APT detection and traceability, industrial control security, and data governance
Received Date: June 04, 2024
Revised Date: July 15, 2024
Available Online: September 13, 2024

Graphical Abstract

Abstract

Abstract

Data identification is a prerequisite for achieving precise data governance, effectively ensuring the security of data elements during cross-domain transfer. Currently, there are methods for generating identifiers for individual data, but as the scale of data continues to expand, identifiers at the data level cannot be directly applied to the dataset level. This also introduces issues of identifiers being “easily damaged” and “difficult to embed”. To effectively address these issues, we adopt the design concept of network honeypoint from the “guardian” model proposed by academician Fang Binxing. Utilizing the idea of deception defense, we propose an anti-damage data identification technology based on dataset honeypoint for cross-domain data transfer scenarios, and design a complete method for generating and embedding dataset honeypoints. First, for cross-domain data transfer scenarios, dataset honeypoints are designed. By enhancing the concealment of dataset honeypoints and increasing their redundancy, the issue of identifiers being “easily damaged” is addressed. Second, by ensuring that the form of dataset honeypoint is indistinguishable from real data, the issue of identifiers being “difficult to embed” is resolved. Finally, experiments conducted on both image and encrypted text data modalities demonstrate that dataset honeypoints possess high anti-damage capability, high robustness, and low performance overhead.
- data cross-domain transfer,
- data identification,
- dataset honeypoint,
- deception defense,
- damage resistance,
- embedding

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References (33)

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