Authorization Mechanism for Multiparty Data Sharing in Social Network

Ramteke, Ashwajit; Talmale, Girish; Raisoni, G. H.

doi:10.15623/ijret.2014.0304056

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…Our model proposes an automated system where decisions are suggested based on the users' previous interactions online. Ramteke and Talmale [39] Sensitivity and trust levels…”

Section: Fine-grainedmentioning

confidence: 99%

Collusion-resistant multiparty data sharing in social networks

Shetty,

Muniyal,

Proothi

et al. 2024

IJECE

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The number of users on online social networks (OSNs) has grown tremendously over the past few years, with sites like Facebook amassing over a billion users. With the popularity of OSNs, the increase in privacy risk from the large volume of sensitive and private data is inevitable. While there are many features for access control for an individual user, most OSNs still need concrete mechanisms to preserve the privacy of data shared between multiple users. The proposed method uses metrics such as identity leakage (IL) and strength of interaction (SoI) to fine-tune the scenarios that use privacy risk and sharing loss to identify and resolve conflicts. In addition to conflict resolution, bot detection is also done to mitigate collusion attacks. The final decision to share the data item is then ascertained based on whether it passes the threshold condition for the above metrics.

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“…Our model proposes an automated system where decisions are suggested based on the users' previous interactions online. Ramteke and Talmale [39] Sensitivity and trust levels…”

Section: Fine-grainedmentioning

confidence: 99%

Collusion-resistant multiparty data sharing in social networks

Shetty,

Muniyal,

Proothi

et al. 2024

IJECE

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MPAC Model for OSNs

Nagpure¹,

Jadhav²

2015

2015 International Conference on Computing Communication Control and Automation

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On today's date 4 out of 5 people use the social networking sites for their social relationship buildings and be connected with their loved ones and friend circle. While building their social contact and relationships they shares lot more data and contents with each other. the social networking sites like Face Book, Google+ ,Twitter may provide the user's own space to share the contents like images , comments , other sharing contents and along with that the privacy policies also. But still there is a problem of privacy and security of data and content when the multiple users belong to the same content or data in any social networking sites. In this paper we propose the new system or an approach which protects the shared data in multiple user environments.Here we formulate the model for access control to overcome the problem of multiuser environment. And also we are providing the policy specification and enforcement mechanism. And also we will formulate the mechanism of authorization for shared data in OSNs. Here we will also focus on the fake identity in social networks and we will resolve that problem also. The scope of this proposed system is there where the privacy and protection of the shared data is important in multiuser environment like OSNs.The objective is to decrease the privacy issues in multiparty access control and to improve the protection and security of the shared data in OSNs when there are multiple parties to control. And to prevent the attack of fake identity on OSNs and provide the authorization mechanism in multiparty environment.

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Comparison between a Machine-Learning-Based Method and a Water-Index-Based Method for Shoreline Mapping Using a High-Resolution Satellite Image Acquired in Hwado Island, South Korea

Choung¹,

2017

Journal of Sensors

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Shoreline-mapping tasks using remotely sensed image sources were carried out using the machine learning techniques or using water indices derived from image sources. This research compared two different methods for mapping accurate shorelines using the high-resolution satellite image acquired in Hwado Island, South Korea. The first shoreline was generated using a water-index-based method proposed in previous research, and the second shoreline was generated using a machine-learning-based method proposed in this research. The statistical results showed that both shorelines had high accuracies in the well-identified coastal zones while the second shoreline had better accuracy than the first shoreline in the coastal zones with irregular shapes and the shaded areas not identified by the water-index-based method. Both shorelines, however, had low accuracies in the coastal zones with the shaded areas not identified by both methods.

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Authorization Mechanism for Multiparty Data Sharing in Social Network

Cited by 3 publications

References 9 publications

Collusion-resistant multiparty data sharing in social networks

Collusion-resistant multiparty data sharing in social networks

MPAC Model for OSNs

Comparison between a Machine-Learning-Based Method and a Water-Index-Based Method for Shoreline Mapping Using a High-Resolution Satellite Image Acquired in Hwado Island, South Korea

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