In the digital era,social media platforms play a crucial role in forming user communities,yet the challenge of protecting user privacy remains paramount.This paper proposes a novel framework for identifying and analyz...In the digital era,social media platforms play a crucial role in forming user communities,yet the challenge of protecting user privacy remains paramount.This paper proposes a novel framework for identifying and analyzing user communities within social media networks,emphasizing privacy protection.In detail,we implement a social media-driven user community finding approach with hashing named MCF to ensure that the extracted information cannot be traced back to specific users,thereby maintaining confidentiality.Finally,we design a set of experiments to verify the effectiveness and efficiency of our proposed MCF approach by comparing it with other existing approaches,demonstrating its effectiveness in community detection while upholding stringent privacy standards.This research contributes to the growing field of social network analysis by providing a balanced solution that respects user privacy while uncovering valuable insights into community dynamics on social media platforms.展开更多
After a composite service is deployed, user privacy requirements and trust levels of component services are subject to variation. When the changes occur, it is critical to preserve privacy information flow security. W...After a composite service is deployed, user privacy requirements and trust levels of component services are subject to variation. When the changes occur, it is critical to preserve privacy information flow security. We propose an approach to preserve privacy information flow security in composite service evolution. First, a privacy data item dependency analysis method based on a Petri net model is presented. Then the set of privacy data items collected by each component service is derived through a privacy data item dependency graph, and the security scope of each component service is calculated. Finally, the evolution operations that preserve privacy information flow security are defined. By applying these evolution operations, the re-verification process is avoided and the evolution efficiency is improved. To illustrate the effectiveness of our approach, a case study is presented. The experimental results indicate that our approach has high evolution efficiency and can greatly reduce the cost of evolution compared with re-verifying the entire composite service.展开更多
基金partially supported by the China Postdoctoral Science Foundation(No.2023M731951).
文摘In the digital era,social media platforms play a crucial role in forming user communities,yet the challenge of protecting user privacy remains paramount.This paper proposes a novel framework for identifying and analyzing user communities within social media networks,emphasizing privacy protection.In detail,we implement a social media-driven user community finding approach with hashing named MCF to ensure that the extracted information cannot be traced back to specific users,thereby maintaining confidentiality.Finally,we design a set of experiments to verify the effectiveness and efficiency of our proposed MCF approach by comparing it with other existing approaches,demonstrating its effectiveness in community detection while upholding stringent privacy standards.This research contributes to the growing field of social network analysis by providing a balanced solution that respects user privacy while uncovering valuable insights into community dynamics on social media platforms.
基金Project supported by the National Natural Science Foundation of China(Nos.61562087 and 61772270)the National High-Tech R&D Program(863)of China(No.2015AA015303)+2 种基金the Natural Science Foundation of Jiangsu Province,China(No.BK20130735)the Universities Natural Science Foundation of Jiangsu Province,China(No.13KJB520011)the Science Foundation of Nanjing Institute of Technology,China(No.YKJ201420)
文摘After a composite service is deployed, user privacy requirements and trust levels of component services are subject to variation. When the changes occur, it is critical to preserve privacy information flow security. We propose an approach to preserve privacy information flow security in composite service evolution. First, a privacy data item dependency analysis method based on a Petri net model is presented. Then the set of privacy data items collected by each component service is derived through a privacy data item dependency graph, and the security scope of each component service is calculated. Finally, the evolution operations that preserve privacy information flow security are defined. By applying these evolution operations, the re-verification process is avoided and the evolution efficiency is improved. To illustrate the effectiveness of our approach, a case study is presented. The experimental results indicate that our approach has high evolution efficiency and can greatly reduce the cost of evolution compared with re-verifying the entire composite service.
