| Title | A GA-based Data Sanitization for Hiding Sensitive Information with Multi-Thresholds Constraint |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Wu, J. M.-T., Srivastava, G., Pirouz, M., Lin, J. C.-W. |
| Conference Name | 2020 International Conference on Pervasive Artificial Intelligence (ICPAI) |
| Keywords | compositionality, data mining, Data models, Data Sanitization, Databases, genetic algorithm, genetic algorithms, Human Behavior, Itemsets, multi-threshold, Optimization, privacy, privacy preservation, pubcrawl, resilience, Resiliency, security, Sociology, Statistics |
| Abstract | In this work, we propose a new concept of multiple support thresholds to sanitize the database for specific sensitive itemsets. The proposed method assigns a stricter threshold to the sensitive itemset for data sanitization. Furthermore, a genetic-algorithm (GA)-based model is involved in the designed algorithm to minimize side effects. In our experimental results, the GA-based PPDM approach is compared with traditional compact GA-based model and results clearly showed that our proposed method can obtain better performance with less computational cost. |
| DOI | 10.1109/ICPAI51961.2020.00013 |
| Citation Key | wu_ga-based_2020 |
A GA-based Data Sanitization for Hiding Sensitive Information with Multi-Thresholds Constraint