scholarly journals Data trustworthiness and user reputation as indicators of VGI quality

2018 ◽  
Vol 21 (3) ◽  
pp. 213-233 ◽  
Author(s):  
Paolo Fogliaroni ◽  
Fausto D’Antonio ◽  
Eliseo Clementini
IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 1382-1397 ◽  
Author(s):  
Maryam Pouryazdan ◽  
Burak Kantarci ◽  
Tolga Soyata ◽  
Luca Foschini ◽  
Houbing Song

2021 ◽  
pp. 106895
Author(s):  
Hong-Liang Sun ◽  
Kai-Ping Liang ◽  
Hao Liao ◽  
Duan-Bing Chen

Author(s):  
Linjun Yu ◽  
Huali Ai ◽  
Dong-Oun Choi

Named data networking (NDN) is a typical representation and implementation of information-centric networking and serves as a basis for the next-generation Internet. However, any network architectures will face information security threats. An attack named interest flooding attack (IFA), which is evolved, has becomes a great threat for NDN in recent years. Attackers through insert numerous forged interest packets into an NDN network, making the cache memory of NDN router(s) overrun, interest packets for the intended users. To take a comprehensive understanding of recent IFA detection and mitigation approaches, in this paper, we compared nine typical approaches to resolving IFA attacks for NDN, which are interest traceback, token bucket with per interface fairness, satisfaction-based interest acceptance, satisfaction-based push back, disabling PIT exhaustion, interest flow control method based on user reputation and content name prefixes, interest flow balancing method focused on the number of requests on named data networking, cryptographic route token, Poseidon local, and Poseidon distributed techniques. In addition, we conducted a simulation using Poseidon, a commonly used IFA resolution approach. The results showed that Poseidon could resolve IFA issues effectively.


2019 ◽  
Vol 30 (05) ◽  
pp. 1950035 ◽  
Author(s):  
Xiao-Lu Liu ◽  
Shu-Wei Jia ◽  
Yan Gu

User reputation is of great significance for online rating systems which can be described by user-object bipartite networks, measuring the user ability of rating accurate assessments of various objects. The clustering coefficients have been widely investigated to analyze the local structural properties of complex networks, analyzing the diversity of user interest. In this paper, we empirically analyze the relation of user reputation and clustering property for the user-object bipartite networks. Grouping by user reputation, the results for the MovieLens dataset show that both the average clustering coefficient and the standard deviation of clustering coefficient decrease with the user reputation, which are different from the results that the average clustering coefficient and the standard deviation of clustering coefficient remain stable regardless of user reputation in the null model, suggesting that the user interest tends to be multiple and the diversity of the user interests is centralized for users with high reputation. Furthermore, we divide users into seven groups according to the user degree and investigate the heterogeneity of rating behavior patterns. The results show that the relation of user reputation and clustering coefficient is obvious for small degree users and weak for large degree users, reflecting an important connection between user degree and collective rating behavior patterns. This work provides a further understanding on the intrinsic association between user collective behaviors and user reputation.


2019 ◽  
Vol 27 (6) ◽  
pp. 2294-2307 ◽  
Author(s):  
Haiqin Wu ◽  
Liangmin Wang ◽  
Guoliang Xue ◽  
Jian Tang ◽  
Dejun Yang

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