本研究提出一套結合階層分群和差分隱私的 HG-DP（Hierarchical Grouping for Differential Privacy）框架，允許使用者根據資料屬性所定義的階層結構進行分群，並在每一組內計算敏感數值的統計資訊。透過這樣的設計，不僅能提升分群策略的語意可解釋性與彈性，也能有效降低敏感度，減少需要加入的雜訊量。
此外，我們也提供視覺化圖表，讓使用者能直觀比較不同階層組合在資訊損失與敏感度上的表現，作為選擇適合分組方式的參考。學生資料集的實驗結果顯示，HG-DP 框架具備兼顧隱私與資料效用的潛力。整體而言，本框架主要是協助使用者評估以及做出知情決策，分組方式仍由使用者自行判斷。;As data applications continue to grow, balancing privacy and utility has become a key challenge. Differential Privacy (DP) offers strong guarantees but often reduces utility due to added noise. To address this, we propose HG-DP (Hierarchical Grouping for Differential Privacy), a framework that groups data based on attribute hierarchies to lower sensitivity before applying DP. This improves both interpretability and utility. We also provide visualizations to help users compare grouping strategies by information loss and sensitivity. Experiments on a student dataset show that HG-DP supports privacy-preserving release while assisting user-driven decisions.
