隱私、效用與公平：基於差分隱私與k-匿名的混合隱私保護

Abstract

在數位時代，資料分析能帶來有價值的洞察，但也可能侵犯個人隱私。傳統隱私保護技術面臨兩難：k-匿名雖直觀易懂，但難以抵禦具有背景知識的攻擊；差分隱私雖提供嚴謹的數學保障，但可能過度降低資料效用性。此外，現有研究多聚焦於隱私與效用的雙維度平衡，較少關注隱私保護機制對不同社會群體的差異性影響。由於資料收集過程中的固有不平衡，隱私保護機制可能無意中放大這種不平衡，對少數群體造成不成比例的負面影響，進而引發公平性問題。 針對此挑戰，本研究提出一套創新的三維隱私分析框架，目標在於：（1）整合 k-匿名與差分隱私的互補優勢；（2）量化隱私參數對隱私保護性、資料效用性與資料公平性的影響；（3）尋找三個維度間的最佳平衡點。 本研究採用 Adult 資料集進行實驗驗證。首先，透過泛化處理建立目標 k=5 的匿名群組；其次，對等價類計數應用拉普拉斯機制，測試 11 個不同的隱私參數 ε 值（0.1 至 10.0）；最後，建立標準化評分體系，以總變異距離（TVD）衡量資訊損失，以均等勝算差異評估公平性，並透過權重敏感度分析驗證參數選擇的穩健性。 實驗結果顯示：（1）在均衡權重配置（0.4/0.3/0.3）下，ε=1.0 獲得最高整合評分（0.764），在隱私保護性（評分 0.800，k=5）、資料效用性（評分 0.899，TVD=0.004）與資料公平性（評分 0.582）三個維度間達到最佳平衡；（2）與純 k-匿名或純差分隱私方法相比，本研究的混合機制在種族公平性方面改善約 20.1%，同時維持 99.09% 的下游應用準確率保持率；（3）權重敏感度分析證實 ε=1.0 在多數應用情境下表現穩健。本研究不僅整合了 k-匿名的直觀性與差分隱私的理論保障，更首次將公平性系統性地納入隱私保護評估，為需要兼顧多重目標的隱私保護應用提供了全面且靈活的決策支援框架。

In the digital era, data analysis provides valuable insights but may also lead to privacy violations. Traditional privacy protection techniques face a dilemma: k-anonymity, while intuitive and easy to understand, struggles to defend against attackers with background knowledge; differential privacy offers rigorous mathematical guarantees but may excessively reduce data utility. Moreover, existing research primarily focuses on the privacy-utility trade-off, with limited attention to the differential impacts of privacy mechanisms across social groups. Due to inherent imbalances in data collection, privacy protection mechanisms may inadvertently amplify these disparities, causing disproportionate negative effects on minority groups and raising fairness concerns. To address these challenges, this study proposes an innovative three-dimensional privacy analysis framework aimed at: (1) integrating the complementary strengths of k-anonymity and differential privacy; (2) quantifying the impact of privacy parameters on privacy protection, data utility, and data fairness; and (3) identifying the optimal balance among these three dimensions. This research employs the Adult dataset for experimental validation. First, generalization processing establishes anonymization groups with a target k=5. Second, the Laplace mechanism is applied to equivalence class counts, testing 11 different privacy parameters ε (ranging from 0.1 to 10.0). Finally, a standardized scoring system is established, using Total Variation Distance (TVD) to measure information loss, Equalized Odds difference to assess fairness, and conducting weight sensitivity analysis to verify the robustness of parameter selection. Experimental results demonstrate: (1) Under balanced weight configuration (0.4/0.3/0.3), ε=1.0 achieves the highest integrated score (0.764), attaining optimal balance across privacy protection (score 0.800, k=5), data utility (score 0.899, TVD=0.004), and data fairness (score 0.582); (2) Compared with pure k-anonymity or pure differential privacy methods, the proposed hybrid mechanism improves race fairness by approximately 20.1% while maintaining 99.09% accuracy retention in downstream applications; (3) Weight sensitivity analysis confirms that ε=1.0 demonstrates robust performance across most application scenarios. This research not only integrates the intuitiveness of k-anonymity with the theoretical guarantees of differential privacy, but also systematically incorporates fairness into privacy protection evaluation for the first time, systematically incorporates fairness into privacy protection evaluation, providing a comprehensive and flexible decision support framework for privacy protection applications requiring multiple objectives.