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Personalized Reputation Management in P2P Networks.

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P2P networks have become increasingly popular in the recent years. However, their open, distributed and anonymous nature makes them very vulner- able against malicious users who provide bad responses to requests from other peers. Motivated by this observation, various solutions for distributed reputation systems have been presented recently. In this paper, we describe the first repu- tation system which incorporates both user-individual personalization and global experiences of peers in the network, for the distributed computation of reputa- tion values. We also present a secure method to compute global trust values, thus assuring identification and isolation of malicious peers. Finally, our simulations show that our system is robust even against attacks from groups of malicious peers deliberately cooperating to subvert it.
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... [ Chirita et al. 2004] also studied how to compute reputation in P2P networks: each peer p i selects a set S i of trusted peers whose trust ratings and past download histories are the most relevant to access the resources p i needs. A personalized (and fully distributed) implementation of the PageRank algorithm is applied to compute the reputation of peers not belonging to S i . ...
... Compared to approaches cited above, our study introduces several novelties. Unlike [Kamvar et al. 2003] and [Chirita et al. 2004], we consider communities of human users rather than P2P networks and, as a consequence, the creation of trust links between members of the same community relies on sociological and psychological factors. Furthermore, we advance the state-of-the-art by proving that we can supply the shortage of user contributed ratings/reviews to calculate HBR scores by means of CBR scores. ...
Article
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In collaborativeWeb-based platforms, user reputation scores are generally computed according to two orthogonal perspectives: (a) helpfulness-based reputation (HBR) scores and (b) centrality-based reputation (CBR) scores. InHBR approaches, the most reputable users are those who post the most helpful reviews according to the opinion of the members of their community. In CBR approaches, a "who-Trusts-whom" network-known as a trust network-is available and the most reputable users occupy the most central position in the trust network, according to some definition of centrality. The identification of users featuring large HBR scores is one of the most important research issue in the field of Social Networks, and it is a critical success factor of many Web-based platforms like e-marketplaces, product review Web sites, and question-And-Answering systems. Unfortunately, user reviews/ratings are often sparse, and this makes the calculation of HBR scores inaccurate. In contrast, CBR scores are relatively easy to calculate provided that the topology of the trust network is known. In this article, we investigate if CBR scores are effective to predict HBR ones, and, to perform our study, we used real-life datasets extracted from CIAO and Epinions (two product review Web sites) andWikipedia and applied five popular centrality measures-Degree Centrality, Closeness Centrality, Betweenness Centrality, PageRank and Eigenvector Centrality-to calculate CBR scores. Our analysis provides a positive answer to our research question: CBR scores allow for predicting HBR ones and Eigenvector Centrality was found to be the most important predictor. Our findings prove that we can leverage trust relationships to spot those users producing the most helpful reviews for the whole community.
... The intensive employment of trusted nodes effectively speed up the convergence in calculating reputation values. Personalized EigenTrust [2] allows each node to individually specify its trusted nodes and the method proposed in [4] recognizes such trusted nodes with the aid of Page Rank. Federated Eigen-Trust [7] introduces the notion of representative nodes to manage the unpredictable leave of trusted nodes. ...
Article
In this paper, we consider the problem of calculating the node reputation in a Peer-toPeer (P2P) system from fragments of partial knowledge concerned with the trustfulness of nodes which are subjectively given by each node (i.e., evaluator) participating in the system. We are particularly interested in the distributed processing of the calculation of reputation scores while preserving the privacy of evaluators. The basic idea of the proposed method is to extend the EigenTrust reputation management system with the notion of homomorphic cryptosystem. More specifically, it calculates the main eigenvector of a linear system which models the trustfulness of the users (nodes) in the P2P system in a distributed manner, in such a way that: 1) it blocks accesses to the trust value by the nodes to have the secret key used for the decryption, 2) it improves the efficiency of calculation by offloading a part of the task to the participating nodes, and 3) it uses different public keys during the calculation to improve the robustness against the leave of nodes. The performance of the proposed method is evaluated through numerical calculations.
... Reputation is used as a means to build and update trust after a certain number of successful transactions [3,15,28,29,34,40,102]. Anonymity, uncertainty, risk, lack of control, and potential opportunism are key elements in most online transactions. ...
... Pro úlohu určování významných genů vznikl algoritmus GeneRank (Morrison et al. 2005; Benzi a Kuhlemann 2012) a graf interakce proteinů byl vyhodnocován algoritmem PageRank Affinity (Voevodski et al. 2009). Hodnocení reputace uživatelů v P2P 41 sítích s využitím PageRanku ukazují Chirita et al. (2004). Použití PageRanku pro predikci vzniku nové hrany mezi dvěma vrcholy v sociální síti můžeme nalézt v (Liben-Nowell a Kleinberg 2007), určování reputace uživatelů sociální sítě PageRankem v (Han et al. 2012;Hao et al. 2012), hlasovací systém využívající sociální síť a PageRank v (Boldi et al. 2009) a vyhledání vůdčích osob na základě vyhodnocení firemní e-mailové komunikace PageRankem v (Berchenko et al. 2011). ...
Thesis
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Tato práce se zabývá výzkumem metod pro hodnocení významnosti vrcholů v rozsáhlých grafových strukturách. Navržené metody jsou aplikovány při vyhodnocení citačních sítí a sítí vytvořených z Linked Data. V úvodu práce jsou popsány cíle, které nás k návrhu nových metod vedly. Následně lze text práce pomyslně rozdělit na dvě části, z nichž první a obsáhlejší část je věnována návrhu metod pro hodnocení autorů vědeckých publikací a druhá část je věnována návrhu metody pro určení klíčových slov textového dokumentu. Společnou vlastností všech navržených metod je použitý algoritmus PageRank. V první části práce je nejprve shrnut aktuální stav poznání v oblasti citační analýzy a zmíněny nejznámější bibliografické databáze a algoritmy, které bývají při citační analýze používány. Zvláštní prostor je věnován popisu algoritmu PageRank, který jsme při výzkumu používali a dále upravovali. Následně první část obsahuje popis návrhu nových metod pro hodnocení významnosti autorů a popis experimentálního ověření jejich kvality. Pro experimenty byly použity datové kolekce CiteSeer, DBLP a WoS, přičemž výsledky získané z kolekce WoS byly, vzhledem k jejím vlastnostem, prohlášeny za nejdůvěryhodnější. Poté, co se prokázala vhodnost nově navržených metod pro hodnocení autorů, jsme provedli další experimenty, jejichž cílem bylo metody ještě více vylepšit. Zde se pro hodnocení autorů ukázalo nejvhodnější parametrizovat PageRank aplikovaný na citační síť publikací významností časopisů, ve kterých byly publikace zveřejněny. Vhodnost navržených metod a platnost vyvozených závěrů byly ověřeny také vyhodnocením specializovaných kategorií WoS. V druhé části práce jsou nejprve zmíněny významné práce z oblasti klasifikace textových dokumentů a z oblasti využití PageRanku pro extraktivní sumarizaci obsahu dokumentu. Následně je popsán návrh naší metody pro volbu klíčových slov textového dokumentu. Tato metoda využívá PageRank a Linked Data, čímž dokáže určit k textu dokumentu vysoce relevantní klíčová slova, která v textu nemusejí být explicitně uvedena. Kvalita navržené metody byla experimentálně ověřena jejím použitím v klasifikátoru dokumentů, který byl aplikován na dokumenty z kolekce diskusních článků 20 Newsgroups a na dokumenty z vlastní kolekce konferenčních Call-for-Papers. Určená klíčová slova byla použita jako vlastnosti dokumentů. Závěrem bylo, že navržená metoda je vhodná zejména v situacích, kdy máme malé množství dat pro natrénování klasifikátoru. Autorovy vědecké přínosy, které jsou popsány v této práci, byly publikovány formou pěti vědeckých článků, z nichž dva byly zveřejněny v časopisech a tři v konferenčních sbornících.
... Ainsi, comme les pairs sont indépendants, ils peuvent essayer d'influencer le calcul afin d'obtenir des scores d'importance plus élevés. [54] propose un algorithme de calcul d'importance synchrone s'appuyant sur le concept de pairs de confiance, appelés hubs. Dans cet algorithme chaque pair choisit un sous-ensemble de pairs auxquels il fait confiance. ...
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... (51); Bayesian approach (work (41)) Gnutella-based routing (58) ; (30) ; (47); Bayesian approach (works (22; 23; 49; 66) ) DHT-based routing(10) ; EigenTrust(42) ; Maximum likelihood technique(32) ...
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