Differential Privacy Algorithm Based on Personalized Anonymity
| Title | Differential Privacy Algorithm Based on Personalized Anonymity |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Li, Y., Chen, J., Li, Q., Liu, A. |
| Conference Name | 2020 5th IEEE International Conference on Big Data Analytics (ICBDA) |
| Date Published | May 2020 |
| Publisher | IEEE |
| ISBN Number | 978-1-7281-4111-4 |
| Keywords | anonymity, Classification algorithms, Clustering algorithms, composability, Data models, data protection, Differential privacy, differential privacy data publishing algorithm, existing anonymized differential privacy, Human Behavior, Metrics, pattern classification, personalized anonymity, personalized k-anonymity model, privacy, privacy protection relevance, pubcrawl, Publishing, quasiidentifier attribute, resilience, Resiliency, security of data, tuple personality factor classification value |
| Abstract | The existing anonymized differential privacy model adopts a unified anonymity method, ignoring the difference of personal privacy, which may lead to the problem of excessive or insufficient protection of the original data [1]. Therefore, this paper proposes a personalized k-anonymity model for tuples (PKA) and proposes a differential privacy data publishing algorithm (DPPA) based on personalized anonymity, firstly based on the tuple personality factor set by the user in the original data set. The values are classified and the corresponding privacy protection relevance is calculated. Then according to the tuple personality factor classification value, the data set is clustered by clustering method with different anonymity, and the quasi-identifier attribute of each cluster is aggregated and noise-added to realize anonymized differential privacy; finally merge the subset to get the data set that meets the release requirements. In this paper, the correctness of the algorithm is analyzed theoretically, and the feasibility and effectiveness of the proposed algorithm are verified by comparison with similar algorithms. |
| URL | https://ieeexplore.ieee.org/document/9101213 |
| DOI | 10.1109/ICBDA49040.2020.9101213 |
| Citation Key | li_differential_2020 |
- pattern classification
- tuple personality factor classification value
- security of data
- Resiliency
- resilience
- quasiidentifier attribute
- Publishing
- pubcrawl
- privacy protection relevance
- privacy
- personalized k-anonymity model
- personalized anonymity
- anonymity
- Metrics
- Human behavior
- existing anonymized differential privacy
- differential privacy data publishing algorithm
- differential privacy
- Data protection
- Data models
- composability
- Clustering algorithms
- Classification algorithms