A Blockchain based Decentralized Video Streaming Platform with Content Protection System

Barua, Suvadra; Talukder, Dipon

doi:10.1109/iccit51783.2020.9392746

Cited by 7 publications

(4 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There have been several proposals for architectural models for storing videos based on edge computing [17] and storage models that use data transfer protocols in peer-to-peer networks [11,18]. In Ref.…”

Section: Blockchain Technology Applied In Streaming Platformsmentioning

confidence: 99%

Towards a New Business Model for Streaming Platforms Using Blockchain Technology

Soares,

Araújo

2024

Future Internet

View full text Add to dashboard Cite

Streaming platforms have revolutionized the digital entertainment industry, but challenges and research opportunities remain to be addressed. One current concern is the lack of transparency in the business model of video streaming platforms, which makes it difficult for content creators to access viewing metrics and receive payments without the intermediary of third parties. Additionally, there is no way to trace payment transactions. This article presents a computational architecture based on blockchain technology to enable transparency in audience management and payments in video streaming platforms. Smart contracts will define the business rules of the streaming services, while middleware will integrate the metadata of the streaming platforms with the proposed computational solution. The proposed solution has been validated through data transactions on different blockchain networks and interviews with content creators from video streaming platforms. The results confirm the viability of the proposed solution in enhancing transparency and auditability in the realm of audience control services and payments on video streaming platforms.

show abstract

Section: Blockchain Technology Applied In Streaming Platformsmentioning

confidence: 99%

Towards a New Business Model for Streaming Platforms Using Blockchain Technology

Soares,

Araújo

2024

Future Internet

View full text Add to dashboard Cite

show abstract

“…An important concern involves the growing availability of explicit and inappropriate content, which poses risks to the well-being, mental health, and overall educational experiences of students. Educational institutions bear the crucial responsibility of safeguarding their students from exposure to harmful content while fostering an environment conducive to learning and personal development [2,3].…”

Section: Introductionmentioning

confidence: 99%

A Hypersensitive Intelligent Filter for Detecting Explicit Content in Learning Environments

Yu,

Yin

2024

JWE

View full text Add to dashboard Cite

In today’s digital age, educational institutions aim to ensure safe learning environments in the light of pervasive explicit and inappropriate content. This study proposes an innovative approach to enhance safety by integrating convolutional neural networks (CNNs) for visual analysis with an intuitionistic fuzzy logic (IFL) filter for explicit content identification. Additionally, it utilizes GPT-3 to generate contextual warnings for users. A large-scale dataset comprising explicit and educational materials is used to evaluate the system. The results show that this hypersensitive filter has high accuracy performance, particularly in handling ambiguous or borderline content. The proposed approach provides an advanced solution to tackle the challenges of detecting explicit content and promotes safer learning environments by showcasing the potential of combining generative AI techniques across various domains.

show abstract

“…From the rst day of the enforcement of the measures, the music platforms available on the market and platforms for streaming content saw an increase in the number of their subscribers and the content they consume. For this reason, international streaming service providers have made recommendations for the need to reduce data rates to ensure the proper operation of networks, as the requirements have been maximized [2].…”

Section: Introductionmentioning

confidence: 99%

Cyberattacks Defense in Digital Music Streaming Platforms by Mobile Distributed Machine Learning

Fan

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Given the massive popularity of digital music industry repositories and their corresponding targeting by cybercriminals, this paper presents an intelligent model for cyberattacks defense in digital music streaming platforms by mobile distributed machine learning. The basic idea of machine learning is to use large data sets to create a model that responds well to inputs it has never processed before. With the increase in data volume and complexity of models, it becomes increasingly challenging to complete machine learning processes in a single machine. Distributed ML was developed to solve this problem, and a standard procedure is completed through the collaboration of multiple servers. With the evolution of mobile devices and the increase in their number, it is possible to create an integrated and compact mobile distributed machine learning (MDML) system that could reduce the workload of servers. A distributed logit polynomial function model is proposed, which is used to model options in distributed binary regression accounting units, which are of low complexity and high stability in noisy environments.

show abstract

A Blockchain based Decentralized Video Streaming Platform with Content Protection System

Cited by 7 publications

References 11 publications

Towards a New Business Model for Streaming Platforms Using Blockchain Technology

Towards a New Business Model for Streaming Platforms Using Blockchain Technology

A Hypersensitive Intelligent Filter for Detecting Explicit Content in Learning Environments

Cyberattacks Defense in Digital Music Streaming Platforms by Mobile Distributed Machine Learning

Contact Info

Product

Resources

About