A Hybrid Protection Method to Enhance Data Utility while Preserving the Privacy of Medical Patients Data Publishing

Medical patient data need to be published and made available to researchers so that they can use, analyse, and evaluate the data effectively. However, publishing medical patient data raises privacy concerns regarding protecting sensitive data while preserving the utility of the released data. The privacy -preserving data publishing (PPDP) process attempts to keep public data useful without risking the medical patients' pri-vacy. Through protection methods like perturbing, suppressing, or generalizing values, which lead to uncertainty in identity inference or sensitive value estimation, the PPDP aims to reduce the risks of patient data being disclosed and to preserve the potential use of published data. Although this method is helpful, information loss is inevitable when attempting to achieve a high level of privacy using protection methods. In addition, the privacy-preserving techniques may affect the use of data, resulting in imprecise or even impractical knowledge extraction. Thus, balancing privacy and utility in medical patient data is essential. This study proposed an innovative technique that used a hybrid protection method for utility enhancement while preserving medical patients' data privacy. The utilized technique could partition information horizontally and vertically, resulting in data being grouped into columns and equivalence classes. Then, the attributes assumed to be easily known by any attacker are determined by upper and lower protection levels (UPL and LPL). This work also depends on making the false matches and value swapping to make sure that the attribute disclosure is less likely to happen. The innovative technique makes data more useful. According to the results, the innovative technique delivers about 93.4% data utility when the percentage of exchange level is 5% using LPL and 95% using UPL with a 4.5K medical patient dataset. In conclusion, the innovative technique has minimized risk disclosure compared to other existing works.