點對點網路系統（Peer-to-Peer，以下簡稱P2P）利用節點間彼此的直接互動來進行電腦資源和服務的分享，提供一個檔案分享交流的平台。但在享受資源分享的同時，卻也產生了一些問題，如檔案不正確或是出現只下載資源卻不分享資源的人（即為free-riders）。為了能確保可以下載到品質良好的資源與解決free-riding的問題，許多研究文獻提出「名聲機制」來量化使用者的行為，藉由名聲機制來協助使用者在選擇下載資源時能有個參考依據，降低存取到不良檔案的情形。
有些名聲機制的名聲值計算是以「提供資源量」為主，若提供資源量愈多名聲值就愈高，名聲值高能存取的資源也就愈多。這樣的機制雖有激勵使用者分享資源的動機，但卻不能確保檔案資源的品質。另外有些名聲值計算是以「提供評價回饋」為主，針對下載資源給予評價回饋，這樣的機制可降低品質不良的檔案，但卻無法避免free-riders情形發生而且每位使用者的評價回饋都必須是誠實的。
為了考量檔案資源的品質、不實的評價回饋與free-riding的問題發生，本研究將提出適宜的名聲機制來解決這些問題。而此機制包含三個部份，第一部分是「動態存取資源策略」，根據使用者本身名聲值去劃分相對應的資源存取層級，該策略不僅提供激勵的效果並有效降低free-riders的問題外，也讓使用者在存取資源上更具有彈性，使用者能夠存取的資源範圍取決於本身名聲值多寡。然而名聲值相近的節點可能會產生一些惡意行為（如不實的評價回饋），為了避免這樣的情形發生，本機制的第二部分「評價回饋有效策略」因應而生。該策略是防止過多不實的評價對資源提供造成影響。最後一部分是「節點名聲值管理」，將使用者在網路上的行為予以量化，以提供「動態資源存取策略」與「評價回饋有效策略」衡量依據之用。
在模擬實驗結果中可發現，本研究的名聲機制確實可根據提供者傳送優劣檔案行為程度與使用者給予真實評價程度的不同予以區別，表示本研究的機制可依使用者的行為表現不同來區分各行為類型的使用者。而在本研究所提出三種劃分層級的基準，根據模擬結果顯示以「下限值」劃分基準效果較佳。除此之外，本研究與其他資源存取策略相互比較，結果顯示本研究的效能比「隨機資源存取」與「靜態層級資源存取」佳。

Peer-to-Peer （P2P） systems that nodes interact directly to share resources and services provide a kind platform of file sharing. Although P2P systems are convenient for getting resources, there are some problems such as inauthentic files or free-rider. In order to download good resources or solve free-riding, many researches provide “Reputation Mechanism” to quantify their behavior. It can assist us in having the references when we are going to download files and reduce the probability of accessing bad files.
A few reputation computing of the mechanisms are based on “amount of resources contribution”. The more they provided the more reputation value they have. Then they can access more resources. Such mechanisms can animate users to share files but they can not ensure the quality of resources. Others are based on “appreciation feedback”. They give some appreciations depending on the files which they downloaded. Such mechanisms can reduce the quantity of bad files. But it can not avoid the free-rider and it have to assume every appreciation is true.
In order to consider the quality of the resources, inauthentic feedback and free-riding, we propose a new mechanism to solve these questions. This mechanism includes three parts. The first part is called “The policy of dynamic accessing files”. It can not only provide the simulative effect to reduce the problem about free-rider but also provide the flexibility of accessing files for users. Users who can access resources range depend on their reputation. In order to avoid the users whose reputation values are close to give wrong appreciations, the second part of this mechanism is called “The policy of valid appreciation feedback”. This part prevents many wrong appreciations. The last part is called “Reputation management,” it can quantify the users’ behavior and provide the reference for first part and second part.
We can discover that our reputation mechanism can differentiate those people who provide good or bad resources and provide truthful or wrong appreciation. That means our mechanism can differentiate any types of users according to their behavior. Besides, we propose three measures for level dividing. The experience shows the “lower bound” efficiency is the best. Finally, we compare with other resources accessing policies. It results that our efficiency is better than them.

摘要 I
Abstract III
謝誌 V
目錄 VI
圖表索引 VIII
1. 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 研究流程 3
2. 文獻探討 6
2.1 架構簡介 6
2.1.1 集中式架構 6
2.1.2 非結構化全分散式架構 7
2.1.3 非結構化部份分散式架構 8
2.1.4 結構化全分散式 9
2.1.5 四種架構優缺點整理 10
2.2 名聲簡介 12
2.3 激勵理論 14
2.4 名聲機制探討 18
2.4.1 Malicious Detector Algorithm（MDA） 18
2.4.2 DCRC&CORC 19
2.4.3 小結 21
3. 研究方法 23
3.1 問題定義分析 23
3.2 研究架構 24
3.3 研究設計 26
3.3.1 運作流程 26
3.3.2 名聲計算機制 27
3.3.2.1 參數定義 27
3.3.2.2 名聲值計算方式 29
3.3.3動態存取資源機制 29
3.3.3.1層級劃分演算法 30
3.3.3.2資源存取策略 33
3.3.3.3評價有效策略 35
4. 模擬實驗設計 37
4.1 模擬實驗環境 38
4.2 模擬相關參數設定 39
4.3 實驗流程說明 39
4.4 程式運作相關模組 41
4.5 結果分析 44
5. 結論與研究限制 51
5.1 結論 51
5.2 研究限制 52
參考文獻 53
附錄A 差值分佈圖 55

[1] 褚麗娟、黃剛銘、蔡坤哲(民93)。網路虛擬社群激勵機制之研究。出版與管理研究，1，61-78。
[2] 吳明蔚、林盈達(民92)。對等式網路技術。電腦與通訊，103，5-12。
[3] Vroom, V.H. (1964). Work and Motivation. New York：John Wiley.
[4] Sai, H.K. and Siu M.L. (2002). Interoperability of Peer-To-Peer File Sharing Protocols. ACM SIGecom Exchanges, 3(3), 25-33.
[5] Runfang, Z., Shanshan, S., and Kai, H. (2005). Trusted P2P Transactions with Fuzzy Reputation Aggregation. IEEE Internet Computing, 9(9), 24-34.
[6] Patricia and Wallace (1999). The Psychology of the Internet. Cambridge, University Press.
[7] Minaxi,G., Mostafa, A., and Paul, J. (2003). A Reputation System for Peer-To-Peer Networks. International Workshop on Network and Operating System Support for Digital Audio and Video, 03. Monterey, California, USA.
[8] Loubna, M., Raouf, B., and Youssef, I. (2004). Detecting Malicious Peers in a Reputation-Based Peer-to-Peer System. Consumer Communications and Networking Conference. 37-42.
[9] Lawler, E.E. and Porter, L.W. (1968). Managerial Attitudes and Performance. Homewood：Richard D.Irwin.
[10] Karl, A. and Zoran, D. (2001). Managing Trust in a Peer-2-Peer Information System*. The first Conference on Information and Knowledge Management, 310-317. Atlanta Georgia, USA.
[11] Jordi, D., Josep, M.P., and Ramon, S. (2002). Extracting reputation in multi agent systems by means of social network topology. The first international joint conference on Autonomous agents and multi-agent systems, 467-474.
[12] Hector, G.M., Mario, T.S., and Sepandar, D.K. (2003).The EigenTrust Algorithm for Reputation Management in P2P Networks. The 12th international conference on World Wide Web, 640-651.
[13] Ernesto, D., Sabrina, D.C, and Stefano, P. (2002). A Reputation-Based Approach for Choosing Reliable Resources in Peer-to-Peer Networks. The second Conference on Computer and Communications Security, 207-216. Washington, DC, USA.
[14] Elodie, F., Kate, L., and William, C. (2006). A Reputation Mechanism for Layered Communities. ACM SIGecom Exchanges, 6(1), 11-22.
[15] Diomidis, S. and Stephanos, A.T. (2004). A Survey of Peer-to-Peer Content Distribution Technologies. ACM Computing Surveys, 36(4), 335-371.
[16] Chrysanthos, D. (2001). Analyzing the economic efficiency of ebay-like online reputation reporting mechanisms. The 3rd ACM conference on Electronic commerce, 171-179.
[17] Audun, J. and Roslan, I. (2002). The beta reputation system. The 15th Bled Electronic Commerce Conference e-Reality：Constructing the e-Economy.
[18] Ari, H., Lik, M., and Mojdeh, M. (2002). Notions of reputation in multi-agents systems：A review. The first international joint conference on Autonomous agent and multiagent systems, 280-287.