The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
En tant que concept issu du domaine social, nous avons soutenu que, dans les réseaux P2P, les comportements de recommandation des pairs et les comportements fonctionnels devraient être explicitement séparés. Nous proposons donc le schéma HopRec qui utilise la capacité de recommandation basée sur les sauts pour améliorer la précision du classement de réputation dans le P2P. réseaux. Nos contributions résident dans les aspects suivants : premièrement, nous adoptons l'idée simple mais efficace de déduire la capacité de recommandation (RA) d'un pair : plus ce pair est éloigné des graines malveillantes initiales, plus ce pair devrait avoir une RA élevée ; Ensuite, le calcul des classements de réputation reflète de manière appropriée les différents RA des pairs. Les résultats de la simulation montrent que, par rapport aux algorithmes de type Eigentrust, HopRec peut être robuste aux attaques sybils et front peers, et permettre une amélioration significative des performances. De plus, nous comparons HopRec avec deux systèmes apparentés, Poisonedwater et CredibleRank, et avons constaté que : dans un environnement P2P hospitalier, HopRec peut obtenir de meilleures performances que Poisonedwater et peut atteindre des performances comparables à celles de CredibleRank, avec moins de frais de calcul que CredibleRank. Enfin, nous montrons également que si les graines initiales bonnes et malveillantes pouvaient être sélectionnées en fonction des diplômes des pairs, alors HopRec et CredibleRank peuvent atteindre des performances parfaites.
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Copier
Yufeng WANG, Akihiro NAKAO, Jianhua MA, "HopRec: Hop-Based Recommendation Ability Enhanced Reputation Ranking in P2P Networks" in IEICE TRANSACTIONS on Information,
vol. E93-D, no. 3, pp. 438-447, March 2010, doi: 10.1587/transinf.E93.D.438.
Abstract: As a concept stemmed from social field, we argued that, in P2P networks, peers' recommendation behaviors and functional behaviors should be explicitly separated, thus we propose the HopRec scheme which uses hop-based recommendation ability to improve the accuracy of reputation ranking in P2P networks. Our contributions lie in the following aspects: firstly, we adopt the simple but effective idea to infer peer's recommendation ability (RA): the farer away that peer is from the initial malicious seeds, the higher RA that peer should have; Then, the computation of reputation rankings appropriately reflects peer's different RA. The simulation results show that, in comparison with Eigentrust-like algorithms, HopRec can be robust to sybils and front peers attacks, and achieve significant performance improvement. Moreover, we compare HopRec with two related schemes, Poisonedwater and CredibleRank, and found that: in hospitable P2P environment, HopRec can obtain better performance than Poisonedwater, and can achieve the comparable performance as CredibleRank, with less computation overhead then CredibleRank. Finally, we also show that, if the initial good and malicious seeds could be selected based on peers' degrees, then HopRec and CredibleRank can achieve perfect performance.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E93.D.438/_p
Copier
@ARTICLE{e93-d_3_438,
author={Yufeng WANG, Akihiro NAKAO, Jianhua MA, },
journal={IEICE TRANSACTIONS on Information},
title={HopRec: Hop-Based Recommendation Ability Enhanced Reputation Ranking in P2P Networks},
year={2010},
volume={E93-D},
number={3},
pages={438-447},
abstract={As a concept stemmed from social field, we argued that, in P2P networks, peers' recommendation behaviors and functional behaviors should be explicitly separated, thus we propose the HopRec scheme which uses hop-based recommendation ability to improve the accuracy of reputation ranking in P2P networks. Our contributions lie in the following aspects: firstly, we adopt the simple but effective idea to infer peer's recommendation ability (RA): the farer away that peer is from the initial malicious seeds, the higher RA that peer should have; Then, the computation of reputation rankings appropriately reflects peer's different RA. The simulation results show that, in comparison with Eigentrust-like algorithms, HopRec can be robust to sybils and front peers attacks, and achieve significant performance improvement. Moreover, we compare HopRec with two related schemes, Poisonedwater and CredibleRank, and found that: in hospitable P2P environment, HopRec can obtain better performance than Poisonedwater, and can achieve the comparable performance as CredibleRank, with less computation overhead then CredibleRank. Finally, we also show that, if the initial good and malicious seeds could be selected based on peers' degrees, then HopRec and CredibleRank can achieve perfect performance.},
keywords={},
doi={10.1587/transinf.E93.D.438},
ISSN={1745-1361},
month={March},}
Copier
TY - JOUR
TI - HopRec: Hop-Based Recommendation Ability Enhanced Reputation Ranking in P2P Networks
T2 - IEICE TRANSACTIONS on Information
SP - 438
EP - 447
AU - Yufeng WANG
AU - Akihiro NAKAO
AU - Jianhua MA
PY - 2010
DO - 10.1587/transinf.E93.D.438
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E93-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2010
AB - As a concept stemmed from social field, we argued that, in P2P networks, peers' recommendation behaviors and functional behaviors should be explicitly separated, thus we propose the HopRec scheme which uses hop-based recommendation ability to improve the accuracy of reputation ranking in P2P networks. Our contributions lie in the following aspects: firstly, we adopt the simple but effective idea to infer peer's recommendation ability (RA): the farer away that peer is from the initial malicious seeds, the higher RA that peer should have; Then, the computation of reputation rankings appropriately reflects peer's different RA. The simulation results show that, in comparison with Eigentrust-like algorithms, HopRec can be robust to sybils and front peers attacks, and achieve significant performance improvement. Moreover, we compare HopRec with two related schemes, Poisonedwater and CredibleRank, and found that: in hospitable P2P environment, HopRec can obtain better performance than Poisonedwater, and can achieve the comparable performance as CredibleRank, with less computation overhead then CredibleRank. Finally, we also show that, if the initial good and malicious seeds could be selected based on peers' degrees, then HopRec and CredibleRank can achieve perfect performance.
ER -