# Comparative Analysis of Local Differential Privacy Schemes in Healthcare Datasets
## 4 Experiments
### Experimental Method  
We applied our approach to fifteen open datasets, namely, seven from the healthcare domain and eight from various sectors, including finance, travel, and others. The following is a brief summary of each dataset used in our experiments. * The US Census (1990) dataset [29] is a raw dataset extracted from the Public Use Microdata Samples person records. Recognized for its widespread usage, this dataset holds significance for evaluating the efficacy of both CDP and LDP approaches. In our experiments, we opt to utilize 10% of the available users within the dataset, considering the substantial scale of available users. The continuous attributes within the dataset have been discretized into five distinct categories. * The Bank dataset [30] has the information for marketing campaigns. Through a discretization process, the dataset has been transformed, converting its continuous attributes into five distinct categories. * The Adult dataset [31] stands out as one of the most widely utilized datasets for assessing the efficacy of CDP and LDP approaches. The continuous attributes within the dataset have been discretized into ten distinct categories. * The Ms Fimu dataset [32] concerns multiple tourism statistics of the frequency of visitors by days and by the union of consecutive days. The continuous attributes within the dataset have been discretized into five distinct categories. * The Nursery dataset [33] is derived from a decision model originally developed to rank applications for nursery schools in the 1980s. The dataset has discretized its continuous attributes into ten distinct categories. * The Diabetes, Stroke, and Hypertension datasets belong to the Behavioral Risk Factor Surveillance System (BRFSS) 2015 [34]. The dataset has undergone a discretization process, resulting in the transformation of its continuous attributes into ten distinct categories. * The NHANES dataset [35] was used in the PWS Cup 2021 [36], the goal of which was to provide anonymized healthcare data. Through a discretization process, the dataset has been transformed, converting its continuous attributes into ten distinct categories. * The Cirrhosis dataset [37] compiles information gathered during a Mayo Clinic trial of primary biliary cirrhosis of the liver. The continuous attributes within the dataset have been discretized into ten distinct categories. * The Lung Cancer dataset [38] comprises information on individuals diagnosed with lung cancer, encompassing various factors such as age, gender, exposure to air pollution, and alcohol consumption. The continuous attributes within the dataset have been discretized into ten distinct categories. * The Skin Cancer dataset [39] encompasses a summary of dermatoscopic images. Organized in a tabular format, the dataset includes a comprehensive collection of crucial diagnostic categories for pigmented lesions. The dataset has undergone a discretization process, resulting in the transformation of its continuous attributes into ten distinct categories. * The NIST [40] Privacy Engineering Program introduced National, Texas, and Massachusetts datasets, initiating the Collaborative Research Cycle (CRC) to stimulate research, innovation, and comprehension of data deidentification techniques. These datasets have undergone a discretization process, resulting in the transformation of its continuous attributes into ten distinct categories. In our experiments, we convert categorical variables into numerical representations using techniques such as Label Encoding, where each category is assigned a unique numerical value. The order of these values is determined alphabetically. We utilize LabelEncoder from the scikit-learn library [41] in Python. It is important to note that the order of numerical values based on the alphabetical order of categories is independent of estimation accuracy. The arrangement is solely determined by the alphabetical sequence of the categories and does not reflect any hierarchy or ranking based on the precision of estimations within those categories.