Analysis of Data Tenure Field and Regulatory Logic in the Internet Era

Abstract

Data competition and data conflict of interest have become the core problems faced by the data industry, and the digital space produces a space-time field completely different from the physical space. Art. 25 GDPR requires the controller to not only implement the legal norms into the processing design but to do so in an effective manner. By explicitly declaring the effectiveness of the protection measures to be the legally required result, the legislator inevitably raises the question of which methods can be used to test and assure such efficacy. Our study of data tenure involves examining its logical characteristics and proposing technical regulatory measures that are effective in protecting data interests and interests. This paper utilizes the logic of field analysis to examine the ownership rules of data from the perspective of abstract legal relations. This paper proposes a data-oriented security architecture based on the expressiveness of data tenure on different subjects, reclassifies data tenure under DOSA architecture, and clarifies the process of data authentication and authorization. A hybrid cryptographic protection system is built that incorporates both the proposed improved M-AES and P-RSA algorithms. The results show that: the similarity of randomly matched documents of different data subjects is lower than 0.72, so the fingerprint similarity threshold is taken as 0.72, and through the calculation of whether the text similarity exceeds 0.72, it can accurately determine whether to carry out data rights. From the perspective of data subject tenure regulation, this paper proposes a proven technical protection method, which provides an innovative path for the protection of rights and interests in the data space. In a word, extending the legal compatibility assessment to the real effects of the required measures opens this approach to interdisciplinary methodologies.

Keywords

• data ownership
• data protection law
• field analysis
• dosa architecture
• data subject
• data interest
• 00a73