Research on Legal Supervision System for Economic Based on Big Data
- DOI
- 10.2991/978-94-6463-222-4_39How to use a DOI?
- Keywords
- Legal Supervision Mechanism; Economic Platform; Big Data Technique; Response Costs
- Abstract
Recently, the utilization of big data for disposing the economic transactions has widely developed and achieve enormous benefits for all users. However, existing economic platforms are more concentrated on the effectiveness response and transaction amount, which ignores the legal and regulation issue in the utilization of big data techniques. Specifically, the model may leak users privacy information or dispose several illegal services for users. Therefore, we involve the concept of legal supervision mechanism by setting certain items, which can assist the platform to rid the illegal issues. In this paper, we propose a novel legal supervision model by utilizing the big data analysis tools, which can prevent privacy information leakage and ensure almost transaction is complying the presupposition legals. From our extensive investigation results and analysis, we can conclude that our proposed analysis model by utilizing big data technique can successful prevent the illegal events with reasonable computation cost.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Yiwen Zhou PY - 2023 DA - 2023/08/28 TI - Research on Legal Supervision System for Economic Based on Big Data BT - Proceedings of the 2023 2nd International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2023) PB - Atlantis Press SP - 365 EP - 370 SN - 2589-4919 UR - https://doi.org/10.2991/978-94-6463-222-4_39 DO - 10.2991/978-94-6463-222-4_39 ID - Zhou2023 ER -