臺灣博碩士論文加值系統

在這資訊爆炸的時代，人們普遍開始使用雲端或是P2P (Peer-to-Peer) 分散式儲存技術解決資料共享和資料儲存的問題，但這兩者在資料的隱私和安全上卻還是不盡完善；雲端儲存技術是把資料存放在一個分散式的系統中，由於系統管理的可信任度或保護資料的技術能力是否充足等問題，集中式的儲存方式難免有資料安全、隱私外洩、被監控的疑慮。而P2P儲存系統則是把資料分散存放在多個獨立的資料儲存節點，然陌生的資料保管者普遍是不可信的，且P2P的儲存節點是不穩定的，因此仍舊存在資料安全或隱私外洩的疑慮。在本研究中，我們先用Fountain Code對資料進行編碼，並將編碼後的碼(code symbol)分別儲存在不同的節點，同時解決了資料隱私和儲存節點不穩定的問題，再者，我們結合社群網路的概念，對分散式的儲存節點進行評估，計算其信任分數來選擇可信任的節點，進一步地，由於社群網路中受委託的朋友彼此可能也是朋友，因此我們提出了一個適用於社群網路分散式資料儲存之防合謀節點選擇方法，並導入遊戲理論，設計節點的信任分數計算方式法，避免互為好友的節點，合謀竊取使用者資料的情況。根據實驗結果顯示，與隨機選擇和傳統最高信任分數優先的選擇方法相比，我們提出的方法可以有效地挑選較不會合謀的儲存節點，有效改善資料安全和隱私保護的問題。

In an era of information explosion, cloud storage or P2P (Peer-to-Peer) distributed storage techniques are used to solve the problem of data storage and sharing. However, the distributed storages techniques still face the privacy issues. Although cloud storage stores data in a distributed manner, the administration and management are still centralized and thus poses the problem of trustworthy and being monitored. In addition, whether the administrator has the data protection capabilities is another problem. On the other hand, P2P distributed storage systems are vulnerable to privacy leakage because data are stored in multiple independent nodes that are not trustworthy nor stable. In this thesis, motivated by the privacy protection requirement of a traffic accident emergency rescue system, we propose a Collusion Avoidance Node Selection Scheme for Social Network-based Distributed Data Storage to protect privacy in a distributed and unstable environment. Specifically, we use Fountain Code to encode data and generate sufficient amount of coding symbols and store them distributed. By this approach, we solve the problems of privacy preserving and node unstable problem simultaneously. To improve the reliability, we propose to select node from one’s social network as the data keepers and design a trust score function to evaluate the nodes in the node selection algorithm. Besides, we further consider the concept of gamming theory to prevent the collusion between data-keepers. In the experiments, we use real datasets to study the performance of our proposed approach. The experimental results show that our approach outperforms the Best Score Selection and Random Selection methods and our approach can avoid collusion effectively and improve the data privacy.

摘要 i
Abstract ii
Content iii
List of Figures v
List of Tables viii
Chapter 1 Introduction 1
Chapter 2 Preliminary 5
2.1 Fountain Code 5
2.2 Gamming Theory 6
2.3 System architecture 7
2.4 Social Network and Trust Score 9
Chapter 3 System Modeling 13
Chapter 4 The Keeper Selection Methods 17
4.1 Method 1: Random Choosing 17
4.2 Method 2: Exploiting Trust Network (Best Score Selection) 17
4.3 Method 3: Exploiting Trust Network with Collusion Avoidance (Greedy Keepers Selection) 18
Chapter 5 Experimental Results 19
5.1 Standard of Different K 20
5.2 Adjust delta to 80 22
5.3 Adjust alpha to 2 25
5.4 Adjust beta to 2 27
5.5 Same K in Different Iterations 29
Chapter 6 Conclusion 32
References 33

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