ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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- All Subjects: Computer security
- Creators: Xue, Guoliang
Description
Peer-to-peer systems are known to be vulnerable to the Sybil attack. The lack of a central authority allows a malicious user to create many fake identities (called Sybil nodes) pretending to be independent honest nodes. The goal of the malicious user is to influence the system on his/her behalf. In order to detect the Sybil nodes and prevent the attack, a reputation system is used for the nodes, built through observing its interactions with its peers. The construction makes every node a part of a distributed authority that keeps records on the reputation and behavior of the nodes. Records of interactions between nodes are broadcast by the interacting nodes and honest reporting proves to be a Nash Equilibrium for correct (non-Sybil) nodes. In this research is argued that in realistic communication schedule scenarios, simple graph-theoretic queries such as the computation of Strongly Connected Components and Densest Subgraphs, help in exposing those nodes most likely to be Sybil, which are then proved to be Sybil or not through a direct test executed by some peers.
ContributorsCárdenas-Haro, José Antonio (Author) / Konjevod, Goran (Thesis advisor) / Richa, Andréa W. (Thesis advisor) / Sen, Arunabha (Committee member) / Xue, Guoliang (Committee member) / Arizona State University (Publisher)
Created2010
Description
The presence of a rich set of embedded sensors on mobile devices has been fuelling various sensing applications regarding the activities of individuals and their surrounding environment, and these ubiquitous sensing-capable mobile devices are pushing the new paradigm of Mobile Crowd Sensing (MCS) from concept to reality. MCS aims to outsource sensing data collection to mobile users and it could revolutionize the traditional ways of sensing data collection and processing. In the meantime, cloud computing provides cloud-backed infrastructures for mobile devices to provision their capabilities with network access. With enormous computational and storage resources along with sufficient bandwidth, it functions as the hub to handle the sensing service requests from sensing service consumers and coordinate sensing task assignment among eligible mobile users to reach a desired quality of sensing service. This paper studies the problem of sensing task assignment to mobile device owners with specific spatio-temporal traits to minimize the cost and maximize the utility in MCS while adhering to QoS constraints. Greedy approaches and hybrid solutions combined with bee algorithms are explored to address the problem.
Moreover, the privacy concerns arise with the widespread deployment of MCS from both the data contributors and the sensing service consumers. The uploaded sensing data, especially those tagged with spatio-temporal information, will disclose the personal information of the data contributors. In addition, the sensing service requests can reveal the personal interests of service consumers. To address the privacy issues, this paper constructs a new framework named Privacy-Preserving Mobile Crowd Sensing (PP-MCS) to leverage the sensing capabilities of ubiquitous mobile devices and cloud infrastructures. PP-MCS has a distributed architecture without relying on trusted third parties for privacy-preservation. In PP-MCS, the sensing service consumers can retrieve data without revealing the real data contributors. Besides, the individual sensing records can be compared against the aggregation result while keeping the values of sensing records unknown, and the k-nearest neighbors could be approximately identified without privacy leaks. As such, the privacy of the data contributors and the sensing service consumers can be protected to the greatest extent possible.
Moreover, the privacy concerns arise with the widespread deployment of MCS from both the data contributors and the sensing service consumers. The uploaded sensing data, especially those tagged with spatio-temporal information, will disclose the personal information of the data contributors. In addition, the sensing service requests can reveal the personal interests of service consumers. To address the privacy issues, this paper constructs a new framework named Privacy-Preserving Mobile Crowd Sensing (PP-MCS) to leverage the sensing capabilities of ubiquitous mobile devices and cloud infrastructures. PP-MCS has a distributed architecture without relying on trusted third parties for privacy-preservation. In PP-MCS, the sensing service consumers can retrieve data without revealing the real data contributors. Besides, the individual sensing records can be compared against the aggregation result while keeping the values of sensing records unknown, and the k-nearest neighbors could be approximately identified without privacy leaks. As such, the privacy of the data contributors and the sensing service consumers can be protected to the greatest extent possible.
ContributorsWang, Zhijie (Thesis advisor) / Xue, Guoliang (Committee member) / Sen, Arunabha (Committee member) / Li, Jing (Committee member) / Arizona State University (Publisher)
Created2016