Use this URL to cite or link to this record in EThOS:
Title: Improving the quality and security of probabilistic search in peer-to-peer information retrieval systems
Author: Richardson, S. A.
ISNI:       0000 0004 5352 0675
Awarding Body: University College London (University of London)
Current Institution: University College London (University of London)
Date of Award: 2014
Availability of Full Text:
Access from EThOS:
Full text unavailable from EThOS. Please try the link below.
Access from Institution:
Commercial web search engines typically use a centralised architecture, where machines are kept at centralised server facilities and are under the control of a central authority. This requires high capital and operating costs, potentially deterring new entrants from offering competing services. A promising alternative is to host the search engine on a peer-to-peer (P2P) network. Volunteers can add their machines as peers, essentially providing computing resources for free. However, search quality and security against malicious behaviour is lower than for a centralised system. This thesis addresses these issues for an unstructured P2P architecture. To improve search quality we develop techniques to accurately estimate the collection global statistics that are required by modern retrieval models, but which may be unavailable to peers. To aid with improving search quality further, we introduce the measure of rank-accuracy to better model human perception of queries, and propose rank-aware document replication policies to increase overall rank-accuracy. These policies assume the query distribution can be inferred from prior queries. For cases where this is not feasible, we propose a rank-aware dynamic replication technique that distributes documents as queries are performed. To improve security, we first develop theoretical models to show how an adversary can use malicious nodes to (i) censor a document, (ii) increase the rank of a document, or (iii) disrupt overall search results. We then develop defences that can detect and evict adversarial nodes. We also study how an adversary may perform these attacks by manipulating the estimates of global statistics at each node, and we develop a defence that is effective when up to 40% of nodes behave maliciously.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available