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標題: | 使用隨機回答技術之差異化隱私矩陣分解模型 Exists or Not: A Differentially Private Matrix Factorization using Randomized Response Techniques |
作者: | Jia-Yun Jiang 姜佳昀 |
指導教授: | 林守德 |
關鍵字: | 推薦系統,協同過濾,差異化隱私,矩陣分解模型,隨機回答, recommendation system,Collaborative Filtering,,differential privacy,Matrix Factorization,Randomized Response, |
出版年 : | 2017 |
學位: | 碩士 |
摘要: | 協同過濾是近年來,最被廣泛使用且效果顯著的一類推薦系統模型。然而,其在對於隱私的維護上有很大隱憂,容易受到資料庫漏洞或不被信任的伺服器攻擊。因此,針對這項問題,本篇研究提出一個使用矩陣分解的框架,這個框架乃是利用客戶端上傳梯度的結構,搭配一個兩階段隨機回答的演算法,以此達到對評分的數值、評分的存在以及已訓練之模型的保護。本研究亦使用差異化隱私對此框架具備的隱私程度進行驗證;並透過實驗,成功於數值型回饋任務及單一回饋任務上皆證明其具備一定程度之功用性。 Collaborative filtering (CF) is a popular and widely-used technique for recommendation systems. However, it has privacy concerns of data leakage caused by untrusted servers. To address this problem, we propose a privacy-preserving framework for one of the robustest CF-based method, Matrix Factorization (MF). With the advantage of the characteristic of MF, this framework is based on gradient-transmission client-server architecture to preserve value of feedback and trained model. On basis of this architecture, we further preserve the existence of feedback by a two-stage Randomized Response algorithm. The privacy of this framework is proved to be with the guarantee of differential privacy. We also conduct experiments on numerical feedback task and one-class feedback task. The results demonstrate that our framework can successfully achieve privacy with certain utility. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/59958 |
DOI: | 10.6342/NTU201700150 |
全文授權: | 有償授權 |
顯示於系所單位: | 資訊工程學系 |
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