## 1 Introduction  
With the advent of the big data era, data have been recognized as essential production factors. To promote the data factor market, ensuring data security is a fundamental requirement. In this context, sensitive data pertain to information that, if disclosed or compromised, has the potential to inflict significant harm upon individuals or society. Sensitive data encompass personal privacy information, such as names, phone numbers, bank account numbers, ID numbers, addresses, passwords, email addresses, educational backgrounds, and medical records. Additionally, this includes enterprise data that are not suitable for public disclosure, such as the company's operational details, IP address lists, and network structure. Effectively addressing the diverse and constantly evolving compliance requirements poses a formidable challenge. As the digital transformation gains momentum, numerous countries' laws and regulations, coupled with security requirements stipulated by industry organizations (e.g., PCI DSS [1], SOX [2], HIPAA, GDPR [3], CCPA [4], etc. ), underscore the importance of identifying and classifying sensitive data as the initial step in data protection. Enterprises are confronted with the task of streamlining their compliance workflows by leveraging simplified technology environments and pre-built templates. This necessitates understanding the precise locations of their data and determining whether additional safeguards are necessary. It also involves identifying both structured and unstructured sensitive data, both locally and in the cloud, that fall under regulatory scrutiny. Subsequently, these data must be categorized and cataloged for ongoing vulnerability monitoring. In recent years, the financial industry has witnessed a rapid acceleration of the open banking model, where data applications are shared between banks and third-party service providers. More than 30 countries and regions worldwide have already adopted or are in the process of adopting this model [5]. Open banking offers numerous advantages, including enhanced customer experiences, the creation of new revenue streams, and the establishment of sustainable service models in markets with limited access to traditional banking services [5]. However, open banking also presents significant challenges, particularly concerning data security. The shared data encompass user identity information, financial transaction details, property, and other sensitive information. This extensive data sharing deepens the risk of data leakage and misuse [6]. To enhance the security of open banking data, we propose a sensitive data processing technique in a cloud-edge collaborative environment, as depicted in Figure 1. Firstly, financial institutions in the central cloud of a bank need to conduct a comprehensive assessment of their data assets to create a visual map of sensitive data before sharing with external parties. Secondly, the data application side (third-party organizations) deploys a regulatory outpost on the edge to ensure the security and compliance of open banking data. The Regulatory Sentinel is an independent software system designed to monitor every step of data operations performed by the application side, including storage, retrieval, and sharing. It also incorporates sensitive data identification, anonymization, watermarking, and records all user data operations for log auditing, leakage detection, data flow mapping, and situational awareness of data security [7]. From the description of the regulatory outpost, it is evident that it deeply integrates into the data processing workflow of the application side, leveraging the characteristics of edge-based data processing. To avoid compromising the overall data processing experience  
Figure 1: Cloud-Edge collaborative framework for sensitive data processing. and incurring significant costs for the application side, the deployment of the Regulatory Sentinel should meet the following requirements:  
1.