A novel approach to rights sharing-enabling digital rights management for mobile multimedia

Zhang, Zhiyong; Wang, Zhen; Niu, Danmei

doi:10.1007/s11042-014-2135-7

Cited by 20 publications

(4 citation statements)

References 19 publications

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“…Secure connections are also essential to avoid DRM license violation. For example, digital right licenses could be linked to hardware profiles for sharing among devices (as illustrated by Zhang et al, 2014) or for controlling the display of protected content as shown by Wang et al (2013). Without such mechanism in place, legitimate users can listen to an insecure connection and overwrite a unique serial number of the device, resulting in protected content delivered to unauthorised devices.…”

Section: Discussionmentioning

confidence: 99%

An adversary model to evaluate DRM protection of video contents on iOS devices

D'Orazio

Choo

2016

Computers & Security

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Section: Discussionmentioning

confidence: 99%

An adversary model to evaluate DRM protection of video contents on iOS devices

D'Orazio

Choo

2016

Computers & Security

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“…pay influential users to advertise a particular product, in order to maximize exposure). Some studies on social network trust model and credential paths finding algorithms were done [24][25][26]. In this special issue, Zhuang et al [27] presented a way of measuring the spread influence of users in microblog.…”

Section: Social Networkmentioning

confidence: 99%

Guest Editorial: Multimedia Social Network Security and Applications

Zhang

Choo

2016

Multimed Tools Appl

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In the increasingly inter-connected society, multimedia social networks (MSN) have become a 'mainstream' tool used by online users to connect and share contents with other users 24/7 in real-time. It is, therefore, unsurprising that MSNs have become a salient area of inquiry by computer scientists and computer security researchers. For example, researchers need to design intelligent computing and soft computing technologies to improve multimedia system functions, efficiency and performance, as well as improving user's sharing experiences (e.g. using recommendation systems and more effective algorithms). Ensuring the security of users and data are also an ongoing topic of interest and importance due to the ease in producing and sharing user and multimedia content using MSNs. In recent years, we have seen advances in multimedia system security and soft computing for MSN applications, such as Bhard^security mechanisms (e.g. protocols and methodologies) and Bsoft^computing methods (e.g. machine learning and rough set), as well as research efforts in trust assessment, risk management and social factors to understand the trade-off between the effectiveness and security in MSNs.This special issue is dedicated to the reporting of state-of-the-art and recent advancements in this emerging area of enquiry. We received 26 submissions for this special issue, of which 12 were accepted for publication (i.e. 46 % acceptance rate). Each paper went through a rigorous peer review process, in addition to multiple follow-up rounds with the authors. A summary of the papers is categorized and presented below.

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“…3 The spammers spread malicious links and annoying messages to OSN users without target, and privacy information is unsafe for the cheating actions 4 and blackmails. 5 To prevent the malicious activities, many schemes such as Access Control 6 and digital rights protection [7][8][9] are proposed. In these schemes, trust degree is usually viewed as the main criterion for security policies to make the privacy management more feasible and effective.…”

Section: Introductionmentioning

confidence: 99%

Trust evaluation based on evidence theory in online social networks

Wang

Qiao

Zhang

2018

International Journal of Distributed Sensor Networks

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Trust is an important criterion for access control in the field of online social networks privacy preservation. In the present methods, the subjectivity and individualization of the trust is ignored and a fixed model is built for all the users. In fact, different users probably take different trust features into their considerations when making trust decisions. Besides, in the present schemes, only users' static features are mapped into trust values, without the risk of privacy leakage. In this article, the features that each user cares about when making trust decisions are mined by machine learning to be User-Will. The privacy leakage risk of the evaluated user is estimated through information flow predicting. Then the User-Will and the privacy leakage risk are all mapped into trust evidence to be combined by an improved evidence combination rule of the evidence theory. In the end, several typical methods and the proposed scheme are implemented to compare the performance on dataset Epinions. Our scheme is verified to be more advanced than the others by comparing the F-Score and the Mean Error of the trust evaluation results.

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A novel approach to rights sharing-enabling digital rights management for mobile multimedia

Cited by 20 publications

References 19 publications

An adversary model to evaluate DRM protection of video contents on iOS devices

An adversary model to evaluate DRM protection of video contents on iOS devices

Guest Editorial: Multimedia Social Network Security and Applications

Trust evaluation based on evidence theory in online social networks

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