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<record>
  <title>Secured hash Techniques for the Analysis of Physical Health Based on Third Party and Apriori Algorithm</title>
  <journal>Journal of Information Security Research</journal>
  <author>Nan Zhang</author>
  <volume>16</volume>
  <issue>1</issue>
  <year>2025</year>
  <doi>https://doi.org/10.6025/jisr/2025/16/1/1-8</doi>
  <url>https://www.dline.info/jisr/fulltext/v16n1/jisrv16n1_1.pdf</url>
  <abstract>This article aims to conduct empirical testing on 6043 students from 7 high schools to explore the data
background and potential risks that third-party physical health measurements can reveal. Through Apriori
Analysis of algorithms, we have concluded that there is a certain degree of similarity between the results and
reference values of most indicators when measured by a third party. Therefore, this paper can effectively
reveal the potential risks to effectively control and reduce the possible risks when preventing and treating
diseases. Through the Apriori algorithm, we found that six rules with more than 10% support showed relatively
small interaction between the Standing long jump and other sports. Therefore, we concluded that the athletes
from seven middle schools had relatively good physical conditions, muscle elasticity, strength, and durability.
In addition, data from third parties can more accurately reflect athletesâ€™ exercise status.</abstract>
</record>
