We propose a method to generate statistically representative synthetic data from a given dataset. The main goal of our method is for the created data set to mimic the between feature correlations present in the original data, while also offering a tunable parameter to influence the privacy level. In particular, our method constructs a statistical map by using the empirical conditional distributions between the features of the original dataset. We describe in detail our algorithms used both in the construction of a statistical map and how to use this map to generate synthetic observations. This approach is tested in three different ways: with a hand calculated example; a manufactured dataset; and a real world energy-related dataset of consumption/production of households in Madeira Island. We test our method's performance by comparing the datasets using the on Pearson correlation matrix. The proposed methodology is general in the sense that it does not rely on the used test dataset. We expect it to be applicable in a much broader context than indicated here.

翻译：我们提出一种从给定数据集中生成具有统计代表性的合成数据的方法。该方法的主要目标是使生成的数据集能够模拟原始数据中存在的特征间相关性，同时提供一个可调参数以控制隐私保护水平。具体而言，我们的方法通过利用原始数据集特征间的经验条件分布来构建统计映射。我们详细描述了用于构建统计映射的算法，以及如何利用该映射生成合成观测数据。我们通过三种不同方式验证该方法：手工计算示例、人工构造数据集以及马德拉岛家庭能源消耗/生产的真实数据集。通过比较数据集的皮尔逊相关矩阵来评估本方法的性能。所提出的方法具有通用性，不依赖于特定测试数据集。我们预期该方法可应用于比本文所示更广泛的场景。