It is necessary to confirm the personal data factors and the rules of verification before conducting personal data detection. So that the detection method can be written in the subsequent implementation of the automatic detection tool. This paper will conduct experiments on common personal data factor rules, including domestic personal identity numbers and credit card numbers with checksums. We use ChatGPT to test the accuracy of identifying personal information like ID card identification numbers or credit card numbers. And then use personal data correlation to reduce the time for personal data identification. Although the number of personal information factors found has decreased, it has had a better effect on the actual manual personal data identification. The result shows that it saves about 45% of the calculation time, and the execution efficiency of the accuracy is also improved with the original method by about 22%, which is about 2.2 times higher than the general method. Therefore, the method proposed in this paper can accurately and effectively find out the leftover personal information in the enterprise.
Many Internet of Things and information exchange technologies bring convenience, cost-efficiency, and sustainability to smart city solutions. These changes have improved our day-to-day quality of life, with impacts on: (a) lifestyle (e.g., automation and robotic reaction), (b) infrastructure (efficient energy consumption), and (c) data-driven management (data sensing, collection, and investigation). It is common to integrate Web-based interfaces and such solutions for developing platforms. When software and hardware components store, retrieve, and transfer such information, people may suffer from personal data leakage. This paper introduces a privacy information detection method, using a data weighting mechanism to save time and cost in finding personal information leaks over Web services. According to an initial evaluation, the proposed method can reduce time by 62.19% when processing 8,000 crawled files, and roll-back verification shows that it maintains 90.08% accuracy for finding marked content.
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