| Title | Overview of Privacy Protection Data Release Anonymity Technology |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Deng, Han, Wang, Zhechon, Zhang, Yazhen |
| Conference Name | 2021 7th IEEE Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS) |
| Date Published | may |
| Keywords | anonymity, anonymity technology, Companies, composability, Conferences, data privacy, data release, Government, Human Behavior, Metrics, privacy protection, pubcrawl, Publishing, resilience, Resiliency, social networking (online), Tools |
| Abstract | The collection of digital information by governments, companies and individuals creates tremendous opportunities for knowledge and information-based decision-making. Driven by mutual benefit and laws and regulations, there is a need for data exchange and publication between all parties. However, data in its original form usually contains sensitive information about individuals and publishing such data would violate personal privacy. Privacy Protection Data Distribution (PPDP) provides methods and tools to release useful information while protecting data privacy. In recent years, PPDP has received extensive attention from the research community, and many solutions have been proposed for different data release scenarios. How to ensure the availability of data under the premise of protecting user privacy is the core problem to be solved in this field. This paper studies the existing achievements of privacy protection data release anonymity technology, focusing on the existing anonymity technology in three aspects of high-dimensional, high-deficiency, and complex relational data, and analyzes and summarizes them. |
| DOI | 10.1109/BigDataSecurityHPSCIDS52275.2021.00037 |
| Citation Key | deng_overview_2021 |
Overview of Privacy Protection Data Release Anonymity Technology