本研究探討如何利用雲端運算來計算龐大圖形中，任意兩點的最短路徑距離，
並同時保護圖形上節點的敏感資訊。為了保護圖形上鄰居隱私與特定敏感路徑，
本研究提出兩種隱私保護，分別為(1) k-skip 最短路徑隱私與(2) 敏感路徑隱私。
敏感路徑隱私可將特定敏感路徑的起點、終點分散到不同外包子圖，讓攻擊 者即使辨識出子圖上的節點，也無法在子圖上計算出兩點的最短路徑距離。
另外，為了更有效率地進行圖形轉換與在雲端環境中計算最短路徑距離，本 研究提出結合 k-skip 最短路徑、節點階層還有由下而上之圖形切割技術，可以避 免事前計算所有最短路徑，並加速最短路徑距離的查詢時間。
在實驗分析中，我們使用圖形產生器產生 3 個不同規模，具有冪次法則與小 世界現象的無尺度網路，並透過變動隱藏鄰居層數與敏感路徑比例來檢驗提出演 算法之特性。首先，實驗結果呈現使用 k-skip 最短路徑技術來保護鄰居隱私，其 圖形轉換效率優於事前計算所有最短路徑再保護鄰居隱私之方法。另外，在實驗 k-skip 最短路徑隱私可將外包子圖上的鄰居隱私保護擴展到 0 至 k-1 層鄰居隱私，
並維持最短路徑距離正確性。結果中顯示當想要隱藏的鄰居隱私層數增加時，處 理時間會快速增加。當需要保護的敏感路徑比例增加時，最短路徑查詢時間與圖 形轉換時間也會隨之增加。最後，切割子圖數量增加，最短路徑查詢時間會隨之 減少，顯示出分散運算之效果。
本研究提出之演算法，可透過調整 k 值增加隱匿鄰居節點的範圍，降低節點 被辨識之機會，並且在無尺度網路為模擬對象的實驗中可有效率地進行圖形轉換 與最短路徑距離查詢。但是，針對增加 k 值能提升多少隱私保護效果，本研究尚 未設計衡量指標來數據化隱私保護之效果。另外，本研究提出之圖形分散演算法 目前只使用於無尺度網路，尚未對其它類型之網路結構做設計考量。
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因此，本研究之未來研究方向可以分為以下兩點進行:
第一，設計能評估本研究隱私保護效果之衡量指標。本研究未來可導入機率 圖模型 (Probabilistic Graph Model, PGM)，用來計算各節點或外包圖出現之機率。
例如採用貝式網路 (Bayesian Network, BN)或隱馬可夫鏈 (Hidden Markov Model, HMM)之機率計算方法，並參考其中模型選擇 (Model Selection)之方法，例如採 用 Akaike information criterion (AIC) 、 Bayesian information criterion (BIC) 或 Mutual information (MI)等指標，來做為本研究隱私保護效果之衡量指標。
第二，設計能應用在其他網路結構之圖形分散方法與最短路徑距離計算方 法。本研究目前適用於無尺度網路結構，比如道路網路、網路拓樸之隱私保護。
未來可基於本研究之三層架構，設計適用於社群網路等有多個團(clique)之網路 結構之圖形分散與最短路徑距離計算方法，以提供各種情境下更好的隱私保護效 果與更有效率的查詢速度。
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