| Title | Improving Data Privacy Using Fuzzy Logic and Autoencoder Neural Network |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Pattanayak, S., Ludwig, S. A. |
| Conference Name | 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) |
| Date Published | jun |
| Keywords | autoencoder, Autoencoder Neural Network, Biological neural networks, cryptography, data privacy, electronic health records, Fuzzy Cryptography, Fuzzy logic, fuzzy logic membership functions, Health Care, health sectors, hospitals, Metrics, neural nets, Organizations, privacy, privacy breach, privacy preserving techniques, pubcrawl, Resiliency, Scalability, security, stored data |
| Abstract | Data privacy is a very important problem to address while sharing data among multiple organizations and has become very crucial in the health sectors since multiple organizations such as hospitals are storing data of patients in the form of Electronic Health Records. Stored data is used with other organizations or research analysts to improve the health care of patients. However, the data records contain sensitive information such as age, sex, and date of birth of the patients. Revealing sensitive data can cause a privacy breach of the individuals. This has triggered research that has led to many different privacy preserving techniques being introduced. Thus, we designed a technique that not only encrypts / hides the sensitive information but also sends the data to different organizations securely. To encrypt sensitive data we use different fuzzy logic membership functions. We then use an autoencoder neural network to send the modified data. The output data of the autoencoder can then be used by different organizations for research analysis. |
| DOI | 10.1109/FUZZ-IEEE.2019.8858823 |
| Citation Key | pattanayak_improving_2019 |
Improving Data Privacy Using Fuzzy Logic and Autoencoder Neural Network