Using reputation measurement to defend mobile social networks against malicious feedback ratings

Huang, Lin; Wang, Shangguang; Hsu, Ching‐Hsien; Zhang, Juanjuan; Yang, Fangchun

doi:10.1007/s11227-015-1432-x

Cited by 14 publications

(6 citation statements)

References 11 publications

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“…For the cloud-based recommendation service, many issues need further investigation. Reputation management techniques should also be involved for support-group's recommendations (Huang et al, 2015). A future research agenda will include utilizing game theory to better formulate virtual societies, multiple preferences release and its impact on patient's privacy.…”

Section: Discussionmentioning

confidence: 99%

Privacy Enhanced Cloud-Based Recommendation Service for Implicit Discovery of Relevant Support Groups in Healthcare Social Networks

Elmisery

Sertovic

2017

International Journal of Grid and High Performance Computing

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Recommending support-groups in healthcare social networks is the problem of detecting for each patient his/her membership to one support-group of relevant patients. The patients in each support-group share some relevant preferences which guarantee that the support-group as a whole satisfies some desired properties of similarity. As a result, forming these support-groups requires the availability of personal data of different patients. This is a crucial requirement for different recommender services. With the increasing trend of service providers to collect a large volume of personal data regarding their end-users, presumably to better serve them. However, a significant part of the data that is typically collected is not essential to the service being offered, or to the completion of the services it was presumably released for. Gathering such unnecessary data can be seen as a privacy threat, and storing it exposes the end-users to further unavoidable risks. In this paper, a privacy enhanced cloud-based recommendation service is proposed for the implicit discovery of appropriate support groups in healthcare social network. A fog based middleware (FMCP) was introduced that runs at patients' sides and allows exchanging of their information to facilities recommending and creating support-groups without disclosing their real preferences to other parties. The membership of patients in various support groups allows receiving highly appropriate and reliable healthcare-related advices. The system utilizes two protocols to attain this goal. Experiments were performed on real dataset.

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Section: Discussionmentioning

confidence: 99%

Privacy Enhanced Cloud-Based Recommendation Service for Implicit Discovery of Relevant Support Groups in Healthcare Social Networks

Elmisery

Sertovic

2017

International Journal of Grid and High Performance Computing

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“…Furthermore, this method makes a real-time response difficult and entails a significant number of arithmetic operation processes, because statistical data by IP should be created every hour and every day, and arithmetic operation should be performed by a combination of such data, in order to estimate the score [22,23]. And, Lin proposed reputation measurement against malicious feedback [24]. Lee implemented a method that is quite simple, but which is equipped with real-time characteristics, by concentrating on factors that can identify a spam e-mail using a single EML only, by removing the ambiguous concept of "score".…”

Section: Related Workmentioning

confidence: 99%

EMICS: E-mail based Malware Infected IP Collection System

Lee¹,

Kwak²

2018

KSII TIIS

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Cyber attacks are increasing continuously. On average about one million malicious codes appear every day, and attacks are expanding gradually to IT convergence services (e.g. vehicles and television) and social infrastructure (nuclear energy, power, water, etc.), as well as cyberspace. Analysis of large-scale cyber incidents has revealed that most attacks are started by PCs infected with malicious code. This paper proposes a method of detecting an attack IP automatically by analyzing the characteristics of the e-mail transfer path, which cannot be manipulated by the attacker. In particular, we developed a system based on the proposed model, and operated it for more than four months, and then detected 1,750,000 attack IPs by analyzing 22,570,000 spam e-mails in a commercial environment. A detected attack IP can be used to remove spam e-mails by linking it with the cyber removal system, or to block spam e-mails by linking it with the RBL(Real-time Blocking List) system. In addition, the developed system is expected to play a positive role in preventing cyber attacks, as it can detect a large number of attack IPs when linked with the portal site.

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“…User-based recommendation systems are important in determining the reputation of mobile social networks. A reputation can be defined as the expectation of a node's behavior based on information from past actions (Huang et al, 2015). The work by Huang et al (2015) used the reputation scores of other nodes to overcome feedback ratings from malicious attacks.…”

Section: Related Workmentioning

confidence: 99%

“…A reputation can be defined as the expectation of a node's behavior based on information from past actions (Huang et al, 2015). The work by Huang et al (2015) used the reputation scores of other nodes to overcome feedback ratings from malicious attacks. An important benefit of this approach is not having to maintain large amounts of historical data and reducing the time complexity to process it.…”

Section: Related Workmentioning

confidence: 99%

A Study and Implementation of a Movie Recommendation System in a Cloud-based Environment

Raigoza

Karande

2017

International Journal of Grid and High Performance Computing

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The availability of huge amounts of data in recent years have led users to being faced with an overload of choices. The outcome is a growth on the importance of recommendation systems due to their ability to solve this choice overload problem, by providing users with the most relevant products from many possible choices. For producing recommendations, things like a user's psychological profile, their browsing history and movie ratings from other users can be considered. To determine how strongly two user's behavior are related to each other, a Pearson correlation coefficient value is often calculated. In this paper, we study the recommendation system on a proposed cloud based environment to produce a list of recommended movies based on a user's profile information. Based on the Software-as-a-Service (SaaS) model implemented, we discuss the concepts such as collaborative filtering and content-based filtering. Given a MovieLens data-set, our results indicate that the proposed approach can provide a high performance in terms of precision, and generate more reliable and personalized movie recommendations, when given a greater number of movies rated by a user. An evaluation was done under minimal known data, which commonly leads to the cold-start problem.

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Using reputation measurement to defend mobile social networks against malicious feedback ratings

Cited by 14 publications

References 11 publications

Privacy Enhanced Cloud-Based Recommendation Service for Implicit Discovery of Relevant Support Groups in Healthcare Social Networks

Privacy Enhanced Cloud-Based Recommendation Service for Implicit Discovery of Relevant Support Groups in Healthcare Social Networks

EMICS: E-mail based Malware Infected IP Collection System

A Study and Implementation of a Movie Recommendation System in a Cloud-based Environment

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