A Machine Learning Model for Data Sanitization
Usman Ahmed, Gautam Srivastava, Jerry Chun‐Wei Lin
Abstract
Discovering important knowledge that may be available from databases while preserving the privacy of sensitive information can be considered a hot research subject of data mining in recent times. With the establishment of strong Internet of Things (IoT) networks globally, several data-intensive applications will be developed. Privacy of information over the network is increasingly relevant, and as edge computing has grown more important, applications running over networks require protection. The privacy of information while utilizing data is a trade-off that needs to be addressed. In the past, many heuristics and meta heuristics-based approaches were revealed to sensitize sensitive information in privacy-preserving data mining (PPDM). They perturb the original database to hide sensitive information using addition or deletion operations. This is a known NP-hard problem. In this paper, we propose data privacy of IoT connected devices over heterogeneous networks. A deep re-enforcement learning-based technique is applied to sensitize sensitive information from a given database while keeping the balance between privacy protection and knowledge discovery during the sanitization process. Furthermore, minimizing known side effects that can be caused in the sanitization process is also be considered. Substantial experiments are conducted on both synthetic and real-world datasets. Results are evaluated based on sanitization side effects which include failing to hide sensitive items as well as choosing not to hide sensitive items. The proposed approach shows significant performance improvement compared to meta-heuristics (Genetic Algorithm, Particle Swarm Optimization) and heuristics (Greedy) approaches by our evaluation.